Unique Vulnerabilities and Attacks on Cellular Data … · 2015-07-28 · formation security rather than network security. We argue that network vulnerabilities can cause We argue

Unique Vulnerabilities and Attacks on Cellular Data PacketServices

By

DENYS MA

B.S. Computer Science and Engineering. (University of California, Davis) 2004

THESIS

Submitted in partial satisfaction of the requirements for the degree of

MASTER OF SCIENCE

in

Computer Science

in the

OFFICE OF GRADUATE STUDIES

of the

UNIVERSITY OF CALIFORNIA

DAVIS

Approved:

Assistant Professor Hao Chen(Chair)

Professor Karl Levitt

Assistant Professor Xin Liu

Committee in Charge

2007

–i–

Unique Vulnerabilities and Attacks on Cellular Data PacketServices

Copyright 2007

by

Denys Ma

ii

Contents

1 Introduction 11.1 Contributions of this Thesis to the Field . . . . . . . . . . . . .. . . . . . . . . . 2

2 Related Works 32.1 Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 32.2 Cloning and Fraud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 42.3 Denial of Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 42.4 Spam and Phishing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 52.5 Worms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.6 3G scheduling and network security . . . . . . . . . . . . . . . . . .. . . . . . . 6

3 Sleep Deprivation Attack 73.1 Background overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . 7

3.1.1 GSM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Location update . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Paging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.1.2 GPRS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.1.3 MMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.2 Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.2.1 MMS security analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .. 12

Unencrypted and unauthenticated MMS messages . . . . . . . . . . .. . 13Unauthenticated MMS R/S . . . . . . . . . . . . . . . . . . . . . . . . . . 13Critical phone information disclosure . . . . . . . . . . . . . . . . .. . . 13

3.2.2 Attack implementation . . . . . . . . . . . . . . . . . . . . . . . . . .. . 13Building target hit-list . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Draining batteries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Theoretical impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2.3 Attack experiment results . . . . . . . . . . . . . . . . . . . . . . .. . . 163.2.4 Attack improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

Attack using TCP ACK packets . . . . . . . . . . . . . . . . . . . . . . . 17Attack using packets with maximum-sized payload . . . . . . . . .. . . . 17NAT and firewall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.3 Mitigation strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . 19

iii

3.3.1 MMS Protocol Modification . . . . . . . . . . . . . . . . . . . . . . . .. 193.3.2 Adaptive PDP Context Management . . . . . . . . . . . . . . . . . .. . . 20

Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Design Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Strategy overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Specification Modification . . . . . . . . . . . . . . . . . . . . . . . . . . 24Analytical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Implementation Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

4 Scheduler Attack 274.1 Attack overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 27

4.1.1 3G data networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Opportunistic scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Handoff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.1.2 Overview of attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . .294.2 Attack analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 30

4.2.1 Attack within a single cell . . . . . . . . . . . . . . . . . . . . . . .. . . 31Single attacker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31Multiple attackers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.2.2 Attack from two cells . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 35Initial average throughput . . . . . . . . . . . . . . . . . . . . . . . . . . 35Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.2.3 Attack without knowing victims’ CQIs . . . . . . . . . . . . . .. . . . . 374.3 Attack impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 404.4 Possible defense strategies . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . 41

4.4.1 Attack detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .414.4.2 Attack prevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 42

4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44

5 Summary and Conclusion 49

Bibliography 51

iv

Acknowledgements

I want to give utmost gratitude to Professor Hao Chen for his most valuable advises, and

guidance for not only this thesis, but also as a graduate student. This work would not exist without

his insights and dedicated work. My gratitude also goes to Professor Karl Levitt for his help and

advises in every step of my graduate life. He encouraged and supported me throughout the years

I’ve been in Davis.

I would like to thank everyone who contributed to this thesis. In particular, most credits to

Radmilo Racic for his extremely valuable contributions. Hehas worked on this work in every aspect

and help me through difficult problems. Also, my thanks to Professor Xin Liu for her contributions

to this thesis.

Many thanks are due to my friends for all the support and advises. My thanks to Dr.

Jeff Rowe and Professor Felix Wu for all the advises on my research, Senthil Cheetancheri for his

insights and discussions on worms and my research, and AllenTing for his encouragement and

support on my efforts. My gratitude to Carol Lin for her endless encouragements and support, even

in difficult times. She believed in me even through periods ofuncertainty, and gave me courage to

proceed.

Finally, I dedicate this thesis to my parents who, through hardship, provided me a chance

to learn and discover as I wish. They have placed my needs overeverything else to support me

throughout my life. They have also shaped me into the person Iam today through valuable wisdom

and guidance.

v

Abstract

As cellular data services and applications are being widelydeployed, they become attractive tar-

gets for attackers, who could exploit unique vulnerabilities in cellular networks, mobile devices,

and the interaction between cellular data networks and the Internet. Furthermore, mobile devices,

often times considered to be part of the cellular network’s trusted computing base (TCB), are be-

coming more vulnerable to attacks. This thesis presents several vulnerabilities on the cellular data

packet services and its applications, and present two particular denial of service attacks. First, we

demonstrate an attack, which surreptitiously drains mobile devices’ battery power up to 22 times

faster and therefore could render these devices useless before the end of business hours. This attack

targets a unique resource bottleneck in mobile devices (thebattery power) by exploiting an insecure

cellular data service (MMS) and the insecure interaction between cellular data networks and the

Internet (PDP context retention and the paging channel). Second, we propose a series of attacks

on 3G cellular packet services that exploit the unverified channel condition reports from mobile de-

vices to their base stations, and user-initiated handoffs.Our simulations show that only five rogue

devices per cell can use up over 90% of the network resource, and thus induce and perpetuate 2.1s

end-to-end inter-packet transmission delay for every userin the cell. This thesis also presents sev-

eral mitigation strategies to defend against not only the two aforementioned attacks, but also similar

attacks of these type.

vi

CHAPTER 1. INTRODUCTION 1

Chapter 1

Introduction

Cellular networks are part of our critical information infrastructure. Cellular networks

are also widely deployed, with more than 194 million subscribers covering over 65% of the US

population. [1] As mobile devices become more powerful, cellular companies are rapidly deploying

broadband data services, such as High-Speed Downlink Packet Access (HSDPA) and Evolution-

Data Optimized (EV-DO) as well as new applications, such as Multimedia Messaging Service

(MMS), Unlicensed Mobile Access (enabling network-to-network mobile agent migration), i-Mode

(providing fast, packet-based communication by eliminating the traditional WAP gateway), and Wifi

Voice-over-IP (enabling affordable, realtime voice communication). Furthermore, cellular networks

are pushing more network functions into mobile devices and grant them more trust. In some situa-

tions, they even consider mobile devices as part of the Trusted Computing Base (TCB). While these

new services and applications enhance mobile computing experience, they also introduce serious

security concerns. Besides launching typical Internet attacks — such as denial of service (DoS),

malware, spamming and phishing — against mobile devices, anattacker can exploit emerging vul-

nerabilities in cellular networks, mobile devices, and theinteraction between cellular data networks

and the Internet.

Emerging vulnerabilities in cellular networks, however are not thoroughly studied, both

by the security community or service providers; since the cellular community are focused on in-

formation security rather than network security. We argue that network vulnerabilities can cause

havoc in cellular networks, in particular, both current andfuture data services. Therefore, this

thesis presents several emerging vulnerabilities in cellular data networks and two particular denial-

of-service attacks exploiting these vulnerabilities thatcan cause devastating affects. These attacks

would be devastating not only in critical situations, such as disasters, but also for industries relying

CHAPTER 1. INTRODUCTION 2

on mobile communications. For example, professions like real estate agents and brokers rely on the

ability to perform on-the-spot credit reports or provide instant quotes. Similarly, occupations such

as network system administrators trust their cellular handset’s availability in order to be reached.

The first attack, exploiting vulnerabilities in MMS and General Packet Radio Service

(GPRS) in GSM, targets mobile devices’ battery power. The adversary is able to drain a mobile

phone’s battery stealthily in 7 hours from the Internet. Thesecond attack, exploiting vulnerabilities

in 3G and 3.5G data packet services and their opportunistic scheduler, demonstrate that malicious

mobile devices can usurp time slots at the expense of honest users, hence denying them network

access.For example, we show that only one attacker per cell that has 50 users can occupy as much

as 89% of the all the scheduling slots indefinitely. Similarly, five attackers per cell can cause and

perpetuate 2.1s end-to-end inter-packet transmission delay for every victim user in the cell, thus

rendering many services useless.

This thesis proceeds by presenting an overview of the related works in cellular network

security in chapter 2. Chapter 3 presents the first attack andthe mitigation strategies that can defend

against it. Chapter 4 presents the second attack, with the possible defense mechanisms. Finally,

chapter 5 concludes this thesis.

1.1 Contributions of this Thesis to the Field

This thesis makes the following contribution:

• We identifies vulnerabilities in 2.5G and 3G data services and applications that relies on

these services, in particular, MMS, GPRS, EV-DO, HSDPA, andthe Proportional Fair (PF)

scheduler.

• We implemented an attack to surreptitiously drain a phone’sbattery up to 22 times faster than

normal, illustrating two key vulnerable components in the current cellular data networks.

• We propose a series of attacks on opportunistic scheduling in 3G data networks, analyze

these attacks mathematically, and explore the effectiveness of these attacks under different

network configurations. Our simulations show that these attacks would devastate the network

by rendering many services useless.

• We propose approaches to mitigate or eliminate the impact ofeach attack.

CHAPTER 2. RELATED WORKS 3

Chapter 2

Related Works

In recent years, significant amount of research efforts havebeen focused on security re-

quirements and threat model evaluation on current and emerging cellular technologies, including

GSM [2–4], GPRS [5–8], CDMA [9], SMS [10], MMS [11], and EVDO [12–14]. These works

identify the following key security requirements in cellular networks: subscriber confidentiality, au-

thentication, privacy, cloning prevention, integrity of information as well as billing, fraud detection,

and safe key management. These works also address security threats such as eavesdropping, im-

personation of a user and network, denial of service, man-in-the-middle attacks, hijacking services,

and compromising authentication vectors. Apropos, researchers evaluated the risk levels of each

of these threats as well. Our work is complementary to these previous efforts to secure cellular

networks. In fact, we focus in two new directions: the end user devices (i.e., power-depletion attack

and defense) and the security interactions between different cellular applications (i.e., the merging

of cellular network and the Internet).

In this chapter, we present an overview of the current research efforts in cellular networks.

2.1 Cryptography

Extensive research has been conducted on the cryptography technologies [15–17]. For

instance, studies like [15, 16] suggest the use of a PKI scheme in the GSM/UMTS network while

[17] proposes the use of a SIM card for authentication and payment of web services by mobile

users. Grecas and colleagues propose introducing public-private key pairs for transactions between

the VLR-HLR as well as MS-VLR. Lo and colleagues, on the otherhand, propose the use of PKI and

stream ciphers for authentication and message encryption/decryption, respectively. They both point


out that the nature of the services constituting the PKI renders telecommunications operators prime

candidates for the PKI implementation. Furthermore, MacDonald and colleagues [17] are convinced

that SIM card can be at the center of an authentication and payment platform for consumption of web

services by mobile users. Cryptographic solutions, while efficiently and elegantly mitigating some

principal concerns in cellular networks, cannot defend against some unique threats to end users, such

as a DoS attack and resource starvation attacks. Our work complements the existing cryptography

mechanisms in order to alleviate additional non-conventional threats unique to emerging cellular

data technologies and applications.

2.2 Cloning and Fraud

Significant research has been done on mobile device cloning and the associated frauds

[18]. In complementary to cryptographic solutions, schemes are developed to defend against cloning

and fraud, such as device and user fingerprinting [19], mobility pattern recognition [20], and usage

pattern recognition [21, 22]. These research studies propose new security mechanisms strictly for

cellular networks. However, most studies stipulate fundamental changes in either architecture or end

user equipment. In order to minimize disturbance of currentimplementation of cellular networks,

our research will focus on utilizing existing security mechanisms to mitigate new attacks that were

not discovered or considered.

2.3 Denial of Service

Denial of Service attacks executed on 2G/2.5G networks alsoattracted a lot of attention,

because resources in cellular networks are much more limited than on the Internet. In particular,

control channels are in danger due to its narrow bandwidth..Agarwal et al. [23] conducted a capacity

analysis of shared control channels used for SMS delivery. They concluded that increasing volume

and message sizes can significantly affect network performance. Then,, Enck et al [24] presented a

denial-of-service attack by sending a sufficient number of SMS messages per second to a range of

cellular phones in the same area. An attacker would need onlya single computer with a broadband

network access in order to disrupt a network in a major city bysaturating control channels shared

between voice calls and SMSs. Traynor el at. [25] follows up on this work by simulating the

attack outlined in [24] using a highly accurate GSM simulator, and presented several mitigation

strategies with supporting simulations. Additionally, [26] warns that paging channel is another


scarce resource that an attacker on the Internet can overwhelm and cause a DoS attack. Finally,

Martin el at. [27] discussed the possibilty of a denial of service attack on mobile devices such

as laptops and PDAs. They outlined three different types of battery draining attacks and presented

experiments to demonstrate the affects of such attack. Nashel at. [28] follows up on the work

by presenting a host-based intrusion detection system to detect battery draining attacks. Our work,

inspired by these previous works, extends previous findingsand presents additional vulnerabilities

both in current and future cellular data services.

2.4 Spam and Phishing

In addition to DoS attacks, spam is another well-known problem in the SMS network [10].

Network providers allow email and web-based interfaces to send SMS messages to individual or

multiple handsets directly. Spammers can also employ phishing [29] to trick users into divulging

private personal information. SMS-based phishing has already been discovered in a small German

cellular provider [30], where users are tricked into sending a reply SMS to a value-added service’s

SMS number, charging a small fee per user. Our first attack in Chapter 3 of building a hit-list of

phone IP addresses and model information was inspired by phishing; however, our approach does

not need the user’s participation or even attention, because such information is reported to our server

automatically by most phones.

2.5 Worms

Computer worms that target cellular networks have also appeared in recent years. Tim-

ifonica worm [31] spreads itself via email attachments. Upon infection, a computer sends SMS

messages to random cell phone numbers belonging to a serviceprovider, Movistar, and thus at-

tempts to cause a DoS attack. A proof of concept worm was developed in early 2005 demonstrating

the effects of a worm outbreak on cellular phone platforms. The Cabir [32] worm, spreading via

Bluetooth on Nokia series 60 handsets running Symbian OS, changes the operating system and

searches for other handsets to infect. An epidemic worm spreading model in mobile environments

was proposed by Mickens et al. [33]. Our work is an extension to these previous works. Using a

hitlist of phone numbers, IP addresses, and model information gathered in our attack described in

Chapter 3, worm designers could write better worms by tailoring to different platforms.


2.6 3G scheduling and network security

Significant amount of research has been conducted on efficient resource sharing in cellular

networks. In particular, opportunistic scheduling algorithms have been studied extensively [34–

36]. However, the existing work focuses on improving systemperformance under various system

constraints and requirements. For example, Choi et al [37] study the effects of Proportional Fair

(PF) scheduler on TCP performance. They conclude that TCp’sminimum RTO is too short and it

leads to unnecessary timeouts under the PF’s scheduling policy. Assaad et al. [38] report the effects

of TCP on HSDPA operation and confirm that the lower the congestion rate of TCP, the higher

the application bit rate is. Their results show that the effects of TCP on application performance

are much higher than on the system capacity due to the use of the high speed shared channels.

Andrews [39] also considered the PF scheduler suggested in the High Data Rate (HDR) data system

and shows that the PF scheduler is unstable under certain conditions. Andrews defines stability as

the ability to keep each user’s queue bounded. Using simulations and models, Andrews describes

six different versions of PF scheduler and shows that all of them are unstable. Finally, Bu et al. [40]

studied PF scheduler in multiple cells and propose a centralPF scheduler to increase fairness. In

contrast to our work discussed in Chapter 4, these studies does not consider potential threats of

malicious users and the corresponding effects on the schedulers used in wireless systems.

Initial studies on network security in 3G networks has also been published in recent years

[41–43], outlining possible threats in the cellular network. Particularly, Sridharan et al. [44] model

the uplink channel from mobile devices and the base station in EV-DO and suggest that malicious

users can modify their power transmission level and cause interference for honest users. Our work

in Chapter 4 differs from their work by concentrating on the downlink, since in 3G networks,

downlink bandwidth is much higher than uplink. Furthermore, these studies do not provide an

actual attack, but only outline possible threats against 3Gnetworks.

CHAPTER 3. SLEEP DEPRIVATION ATTACK 7

Chapter 3

Exploiting MMS Vulnerabilities to

Stealthily Exhaust Mobile Phone’s

Battery

In this chapter, we present an attack that exploits vulnerabilities in MMS (a cellular data

service), PDP context retention in GPRS (interactions between the Internet and cellular data net-

works), and the paging channel. Furthermore, this attack has unique features that (1) it is clandestine

– victim mobile users will not notice when their batteries are being drained; (2) it is not limited to

certain mobile device hardware or software; and (3) it targets individual mobile devices rather than

the network, an attack that is often harder to detect and defend effectively by network operators.

We implemented this attack in two stages. In the first stage, we were able to build a fairly

accurate ”hit-list” of all the users with an active Internetconnection by taking advantage of the

insecure MMS protocol. In the second stage, we exploit the PDP context retention to surreptitiously

drain a phone’s battery up to 22 times faster than normal. This attack illustrates two key vulnerable

components in the cellular data network, and we will proposemitigating strategies for securing

these components.

3.1 Background overview

To help understand the vulnerabilities and attacks that we discovered, we present an

overview of the relevant components in cellular networks: GSM, GPRS and MMS.


3.1.1 GSM

The key elements in GSM are: the Base Station Subsystem (BSS), which includes the

Base Transceiver Station (BTS) and the Base Station Controller (BSC), and Mobile Switching Cen-

ter (MSC) which is the core of the Network Sub System (NSS). Additionally, these GSM elements

utilize databases like Home Location Register (HLR) and Visitor Location Register (VLR) for stor-

ing users’ home as well as roaming information, respectively.

BTS provides the means to transmit and receive radio signalsas well as encrypt and

decrypt communication with the BSC. BSC provides network intelligence by allocating radio chan-

nels, controlling inter-BTS hand-offs and, most importantly, serving as a gateway to the MSC. MSC,

on the other hand, sets up circuit-switched communications, takes care of mobility management and

manages other databases.

A cellular network needs to keep track of the location of eachMobile Station (MS1) in

order to deliver calls and data to the correct destination reliably. Typically, the network utilizes an

event-based mechanism to collect mobile device’s location. Events such as powering up, shutting

down, and crossing into another location area are events that trigger the location update procedure.

A cellular network is partitioned into cells serviced by BTSs. Cells are then grouped

together to optimize signaling and to facilitate tracking of mobile phones within the network. Each

group, managed by one BSC, is identified by a location area code broadcast by each BTS at regular

intervals. Two fundamental operations within the locationarea arelocation updateandpaging.

Location update

The MS sends location update messages to its current BTS periodically in order to route

all incoming calls or data appropriately. If the MS sends updates seldom, its location is unknown

and the MS must be paged for each downlink packet (or call), thus degrading the quality of service.

If, on the other hand, the MS sends frequent updates and its location is known, then data packets

can be delivered without any additional paging delay.

Paging

To minimize the amount of updates, preserve MS’s battery, and minimize bandwidth uti-

lization, the network will page the MS over the Paging Channel (PCH) to determine its location. In

1MS and phone will be used interchangeably.


GGSN

SGSN

VLR

MS BSS

MSC

SGSN on another PLMN

Internet

HLR

Figure 3.1: GPRS infrastructure

other words, PCH is used for communication from BTS to MS whenMS is not assigned a traffic

channel; that is, the MS’s location is unknown or out of date.

The paging bandwidth burden is relatively small in small location areas - less than 1% of

the bandwidth allocated for voice channels. On the other hand, in an area with a large number (over

1000) of cells per location area, the paging bandwidth burden could be considerably higher. [45]

3.1.2 GPRS

GPRS [46] is integrated into the existing GSM infrastructure with a new class of network

nodes called GPRS Support Nodes (GSNs). GSNs are responsible for the delivery and routing of

data packets to and from the mobile network. There are two types of GSNs: Serving GPRS Support

Node (SGNS) and Gateway GPRS Support Node (GGSN). SGSN is responsible for transferring

and routing of data packets, mobility management, logical link control, authentication and billing

services within its service area. GGSN acts as an interface between the GPRS backbone and external

packet networks (primarily the Internet). Its primary function is to convert GPRS packets coming

from the SGSN to IP packets and vice versa. An illustration ofGPRS is shown in Figure 3.1.

Before an MS can utilize GPRS services, it must register withan SGSN so all packets

can be routed through it. During this procedure, called GPRSattach, a PDP (Packet Data Protocol)

context is created. In particular, SGSN checks if the user isauthorized, copies the user profile from

the HLR to itself, assigns a Packet Temporary Mobile Subscriber Identity (P-TMSI)2, maps it to an

IP address, and assigns a GGSN that will serve as the gateway to the Internet. The PDP context,

2The reasoning is to minimize use of IMSI (International Mobile Subscriber Identity) for security purposes.


composed of the above mentioned information, is stored at the SGSN. GPRS detach, on the other

hand, disconnects the MS from the GPRS network and deactivates the PDP context.

Location areas have been proven to be efficient in voice networks; however, the bursty

nature of data traffic increases the number of paging messages per phone in each location area.

Therefore, each location area is further subdivided into routing areas used by GPRS to decrease

the penalty for locating an MS. GPRS phones utilize IDLE, STANDBY and READY states in

increasing order of battery consumption. When an MS is in theREADY state, SGSN is aware of

the MS’s location. In particular, the MS performs frequent location updates to provide the network

with the actual cell ID so that no paging is necessary. When inthe READY state, the MS can send

and receive data. Furthermore, it will stay in the READY state until READY timer expires, at which

it will transition to the STANDBY state. While in the STANDBYstate, the MS has established the

PDP context and it can receive calls or data. However, its location updates are more coarse, in the

sense that it informs the SGSN of only routing area changes, but not cell changes. If SGSN needs

to deliver data to the MS while the MS is in the STANDBY state, SGSN will send a page request

in the routing area where the MS is located. When MS responds to the page, it will transition to the

READY state. IDLE state is the lowest battery consumption state, in which the SGSN is not aware

of the MS’s location. The MS can transition out of IDLE state only if it performs a GPRS attach

procedure. Alternatively, an MS could initiate a GPRS detach procedure to transition to the IDLE

state. Figure 3.2 shows the state machine of the GPRS MS.

Upon completion of the communication, the MS will go into a STANDBY mode. The

PDP context, on the other hand,will remain allocated to the MS. We conducted experiments to

discover how long each handset retained its assigned PDP context and IP address. We found that

addresses seemed to be relinquished in as short as 15 minutesto as long as several hours. The reason

for not deactivating a PDP context is simple: a cellphone canbe unavailable for a period of time due

to radio link failure; deactivating and activating a new context would imply that the phone would

need to recreate all TCP sessions, possibly restarting applications and requiring the user to re-enter

all the passwords.

3.1.3 MMS

MMS has become a very popular cellular message service. The MMS architecture spans

both the cellular network and the Internet and uses technologies in both networks, such as WAP,

SMTP, and HTTP.


IDLE

READY

STANDBY

GPRS Attach

READY timer expired

Force to STANDBY

GPRS Detach

Data transmit or receive

STANDBY timer

expired

PDP CONTEXT INACTIVE

PDP CONTEXT ACTIVE

Figure 3.2: The GPRS mobile station state machine

The MMS architecture consists mainly of the MMS Relay/Server (MMS R/S) and user

agents. Several optional entities of the architecture – thebilling server, the Home Location Register,

and the User Database — may exist inside or outside MMS R/S. Figure 3.3 shows an overview of

the MMS architecture.

The MMS R/S is responsible for all of the transactions of MMS.When a user transmits

an email or an MMS message, the mobile phone formats these messages in Synchronized Multime-

dia Integration Language (SMIL) [47]. The MMS R/S translates (transcodes) the message to either

email or different MMS formats depending on the provider. The message is then sent to the destina-

tion SMTP mail server or the destination MMS R/S using SMTP. Upon receiving the message, the

destination MMS R/S then stores the message in the user’s buffer while sending a notification mes-

sage to the user via a SMS or WAP push message. The notificationmessage contains the location of

the message, usually specified as an HTTP address. User can configure their mobile phones either

to automatically download the message upon receiving the notification or to manually download the

message themselves.


MMS R/S

Wireless Network Internet

HLR User dB

User Agent

MM1

MM1

MM4

SMTP

Email Client

Billing Server

MM9 MM5

MM8

Figure 3.3: MMS Infrastructure

3.2 Attacks

In this section, we present our findings on attacking the cellular network. We first inves-

tigated the MMS protocol and discovered several vulnerabilities through which we leveraged into

the heavily protected cellular network. Then, by exploiting these vulnerabilities, we implemented a

proof-of-concept attack on a scarce resource – the battery power – of mobile devices. The attack is

stealthy, as it is noticeable to neither mobile users nor network operators. Our experiments demon-

strate that unique threats against cellular networks and mobile devices exist and are exploitable.

Finally, we discuss how to make this attack even more effective.

3.2.1 MMS security analysis

To test how cellular providers implement MMS and gain insight into their interface de-

signs, we setup our own MMS R/S, based on an open-source project [48]. We discovered several

vulnerabilities that a wily attacker could exploit, as described in the following sections.


Unencrypted and unauthenticated MMS messages

We confirmed that MMS messages and MMS notification messages,composed of headers

and content sections, were sent in plain-text. In addition to the SMIL headers, the packet also

included an HTTP POST header containing the source and destination IP address, the profile of the

user agent, the content type and size, and the user agent name.

Unauthenticated MMS R/S

To mitigate the problem of unencrypted messages, cellular providers hide their own MMS

R/S’s IP addresses in the phones, hoping that cellular userscannot read or overwrite them. Unsur-

prisingly, we discovered that this attempt atsecurity by obscurityis broken.

In order to inspect the MMS message raw format, we modified a phone’s firmware to

route all MMS messages through our MMS R/S. The MMS R/S setting is well hidden in our phone’s

firmware, which suggests that providers do not intend to allow users to modify the setting. After

modifying the MMS R/S entry in our phone, we discovered that the phone had no security mech-

anism to alert the new, unauthorized MMS R/S. Furthermore, MSs also do not authenticate MMS

notification messages and MMS messages sent from the network. MSs will accept any MMS mes-

sages as long as the format is correct. Consequently, we wereable to send unlimited MMS messages

for free, without alarming the cellular provider.

Critical phone information disclosure

We discovered that handsets include pertinent user agent platform information whenever

they communicate over HTTP. Accordingly, we set up a web server runningetherealto capture

HTTP requests from various handsets on different networks.We found that every phone disclosed

either its full profile or information that included one or more of the following: hardware platform

description, display capabilities, and the current and compatible software. An attacker could write

a script that extracts the model number of each handset very easily.

3.2.2 Attack implementation

Based on our MMS security evaluation, we implemented a battery draining attack utiliz-

ing a hit-list built using superfluous but pertinent information disclosed during MMS exchanges.

Figure 3.5 illustrates the attack.


Figure 3.4: Ethereal reconstruction of an MMS message captured by our MMS R/S. The messageis transported in clear text. Various fields such as the server, the sender’s phone number and phonemodel are exposed and could be collected in a the hit list.

Attacker

MMS Server

Victim 1 (1 1 )

(2 1 )

(3 1 )

Victim n

. . .

(1 n )

(2 n )

(3 n )

Figure 3.5: A two-step attack on cellular devices. In Step 1,the attacker builds a hit list using MMSmessage notifications (Messages (1)s), and captures information about mobile users from the HTTPrequests from mobile users (Messages (2)s). In step 2, the attacker drains the batteries of cellulardevices on the hit-list surreptitiously by sending UDP packets (Messages (3)s) periodically to thecellular devices.


Building target hit-list

To launch effective, large scale attacks, an attacker needsto build a hit-list that contains

important information about the network and end users. One way to obtain such information is by

asking the mobile phones.

An attacker can send MMS notification messages, whose content address is at a malicious

web server, to numerous recipients. The target phone numbers can be generated automatically using

known area codes and prefixes for cellular phone numbers. TheMMS notification messages can be

sent using SMS or WAP push. There are many free SMS messaging websites, including those

offered by cellular providers.

Once MMS notification messages are sent, the attacker waits for HTTP request messages

at his web server, which has stated its location in the MMS notification message. Since many cell

phones are configured to download MMS messages automatically upon receiving notification, they

will make HTTP requests to the attacker’s web server. The HTTP requests often contain the profiles

and IP addresses of the phones, and even file extensions that the phones are able to process. By

sending a slightly different URL to each phone, the attackercan build a hit list that maps each

phone number to a profile of its cellular device. More importantly, the phone’s response to the

MMS notification message activates a PDP context, making ourattack easy and simple to execute

even in the presence of NAT and firewalls.

Draining batteries

Using the hit-list generated from MMS notification messages, an attacker can target the

cellular network and cellular devices more precisely and effectively. Apropos, we implemented a

battery draining attack that focuses on the end hosts instead of the network. We implemented our

attack using UDP packets (we will explain an improved technique later.)

The key to maximizing a cell phone’s battery life is to use itstransceiver sparingly. In fact,

when a cellular phone is turned on, its transceiver is activeless than 3% of the time. As a reference,

in wireless sensor nodes, transmitting one bit of information consumes 1500 to 2700 times as much

energy as executing one instruction [49]. Thus, if a packet is sent to a phone, the SGSN will deliver

the packet if the phone’s location is known, or attempt to locate the phone by sending a page request

to it. However, since cellular phones spend most of their time in the STANDBY mode (or other

dormant modes), the page on the paging channel will awaken the phone to the READY state and

force it to perform a location update. The sine qua non of thisattack is to keep the phone in the


READY state (high battery consumption), therefore disabling its ability to preserve battery life, or

to let the phone temporarily go into the STANDBY state only tobe immediately awakened with a

page and forced to perform a location update; both of these actions consume much energy.

Theoretical impact

To investigate the severity of the aforementioned attack, we estimate the damage that an

attacker with a home DSL Internet connection can inflict. A typical DSL upload speed ranges from

256kbps to 416kbps. We use the medium speed,B = 384kbps, for the upload bandwidth as an

estimate. Each UDP packet consists of a character in the datasegment, which might be padded to

4 bytes depending on the provider’s DSL modem. The UDP packetheader has 8 bytes, and the IP

header has 20 bytes. In the pessimistic estimate where our data is padded, the total size of the packet

is S= 32 bytes. Therefore, the maximum number of UDP packets per second that an attacker may

send is(B/8)/S= 1500.

To attack a phone effectively, an attacker must send one UDP packet to the phone every

T seconds. In this case, the maximum number of phones that the attacker can attack simultaneously

is (B/8)∗T/S. We estimated the timeT by trial and error using different test configurations. For

our experiment, we chose 3.75 seconds for the GSM-based network and 5 seconds for the CDMA-

based network. Using our equation, we calculated that an attacker can attack about 5625 phones

using a standard ADSL line for a GSM-based network and around7000 phones for a CDMA-based

network.

3.2.3 Attack experiment results

We successfully drained our test phones’ batteries considerably faster than our average

usage. We conducted six test runs on a high-end Nokia smart phone and completely drained its

battery in an average of 7 hours, instead of 156 hours in normal usage with bluetooth switched

off most of the time. We also observed severe battery exhaustion in our Sony Ericsson test phone,

where the battery was drained down to 20% within less than 7 hours without talking and with

bluetooth switched off. If a phone is connected to the Internet continuously (for example, to use the

instant messaging service), its battery life would be reduced much faster. To test this hypothesis,

we attacked our Motorola test phone while connecting it to the Internet continuously. Our test

completely drained its battery within 2 hours. Table 3.1 summarizes the results of our attack.

We successfully conducted our attack on two major cellular service providers without


Phone Battery Life Without Attack Battery Life Under AttackNormal Use (hours) Standby (hours) Normal Use (hours) Reduction

Nokia 6620 156 200 7 22.3:1Sony-Ericsson T610 60 315 7 8.6:1Motorola v710 36 150 2 18.0:1

Table 3.1: Reduction of battery life due to our attack

triggering any alarms. Our test machine’s IP was not blocked, our phones were fully operational

after the attacks, and no notifications or warnings were sentto us regarding this issue. Moreover,

during the attack the phone appeared to be operating normally and no additional Internet application

was started, so the victim user would not notice the attack, until his/her battery died unexpectedly.

3.2.4 Attack improvement

There are several optimizations that could be done to improve our attack. Currently, we

empirically determined a fixed interval between each UDP packet by trial and error. However, by

using Qualcomm’s CAIT software or knowing the implementation of a particular cellular network,

we could obtain more accurate wait-time and thereby improveefficiency of our attacks. Also,

knowing which IP addresses are vacant would increase the efficacy of our hit-list creation. We are

currently in the midst of testing the following improvements to our attack.

Attack using TCP ACK packets

To force a phone to send as well as receive useless data, an attacker can periodically send

TCP ACK packets to the phone’s IP address. In accordance withRFC793, if the connection is reset

or in half-open state, the receiver of an out-of-order ACK packet will send an RST packet. If, on the

other hand, the connection is open, the receiver of an out-of-order ACK packet will reply with an

empty packet. Either way, an attacker will force a phone thatimplements a full TCP stack to receive

as well as send packets, thereby exacerbating the power consumption.

Attack using packets with maximum-sized payload

In implementing our previous attack, we used UDP packets with no payload in order

to maximize the number of UDP packets an attacker can send percomputer. However, this is

not the most efficient method of draining a cellular phone’s battery, since the whole packet must


be downloaded to the mobile phone before the phone can discard the packet. Therefore, with an

accurate hit-list collected using MMS above, the attacker can sacrifice the number of targets per

his/her computer to deliver an even more efficient attack using a maximum-sized payload.

Using the original attack implemented with UDP, the attacker can send a maximum theo-

retical UDP data packet of 64Kb due to its 2 byte total length field. In the TCP variety of the attack,

ACK messages ”piggyback” onto the existing payload with a maximum size of 1500 bytes. Besides

causing additional unnecessary downloads for the mobile agent, the attack could possibly be even

more efficient due to packet fragmentation. This exacerbates the attack so that the attacker would

only need to send a single packet that becomes multiple packets at the mobile agent.

NAT and firewall

Through field experimentation, we have determined that mostproviders who utilize NAT

also implement Network Address and Port Translation (NAPT.) NAPT provides dynamic(pri-

vateIP, privatePORT)to (publicIP, publicPORT)translation. For example, the inside interface tuple

(10.0.0.5,3000) could be mapped to the outside interface tuple(199.156.3.4,6000).

However, there are certain issues with network-wide NAT deployment. For example, it

often hinders application deployment. Additionally, certain security protocols such as IPSec and

Kerberos are affected – NAT changes the address in the IP header, causing loss of integrity. For

these reasons, operators choose to implement NAT only on certain subnets affecting a selected

customer base. In other words, most operators offer both private and public IP plans.

It would seem that our attack could be mitigated with NAT and firewall placement. How-

ever, a very simple restriction to the attack could yield thesame result. The crux of the change

would be an observation that each inside IP address maps to a port on the outside interface because

the publicIP is the public IP address of NAT system. Thus, targeting an inside IP address reduces

to targeting a certain port of the outside interface. Since NAPT does address and port translation

dynamically, the IP address and port mappings are only aliveduring active PDP contexts. Thus,

the attack must be delivered within an active session window. Since phones automatically create

an outbound connection to connect to a malicious HTTP server, the server itself must deliver the

attack, thus prolonging the connection. The firewall would consider this connection valid as it is

internally initiated over allowed ports, and NAT would continue the address and port translation for

the duration of the attack.


3.3 Mitigation strategies

Our attack uncovered two vulnerable components in cellularnetworks.

• PDP Context is retained.We observed that a mobile user’s PDP context is kept alive even

after the user has completed his/her data session. The PDP context may be kept active from 15

minutes to several hours, depending on the service provider. This active PDP context allowed

us to send unwanted IP packets to the victim’s mobile phone todrain its battery.

• Attack packets are not in any active session.Our attack periodically sends packets to mobile

user without an active connection. A mobile user must initiate active connections before

he receives data. Since the GGSN records the connection states, it can distinguish attack

packets from normal packets that belong to active connections, unless the attacker can guess

the correct sequence number, destination IP address and port number of an active connection.

Based on these observations, we suggest the following mitigation stratgies on MMS and

GPRS.

3.3.1 MMS Protocol Modification

To mitigate threats against MMS, we propose a redesign by incorporating security mech-

anisms into the protocol.

• Message and server authentication. To avoid man-in-the-middle attacks, we should authenti-

cate MMS messages and R/Ss, using PKI for instance.

• Information hiding at WAP gateway. WAP gateway should prevent outside web servers from

obtaining critical information about mobile devices, suchas their IP addresses, and hardware

and software profiles. Since profiles are used only by the WAP gateway for converting web

contents, the WAP gateway should filter out all but essentialinformation about the user agent

in HTTP requests.

• MMS message filtering. Service providers typically hard-code their approved MMSR/S into

mobile devices’ OS or firmware to prevent users from choosingalternative MMS R/Ss. How-

ever, sophisticated users can modify their OS or firmware to defeat this protection. A more

reliable approach for service providers is to filter MMS messages, since all MMS packets

must traverse the provider’s network. The filter can scan MMSmessage headers to ensure


that the destination IP address is one of the MMS R/S or accredited third party Value Added

Service (VAS) providers. The filter should not be implemented at the WAP gateway, but

rather at the SGSN or GGSN, since users can easily modify the phone’s settings and bypass

the cellular provider’s WAP gateway.

3.3.2 Adaptive PDP Context Management

In addition to protocol modification, we suggest a defense framework that could avoid the

shortcomings of external firewalls and IDSs mentioned aboveby supplementing these protection

mechanisms:

• This defense mechanism can also serve as an event detector for IDSs already in place to

monitor the internal network.

• It must also be effective against insider attacks, where malicious users are connected using

the cellular network instead of the Internet.

• It should be designed with the goal of being non-intrusive sothat it does not require ancillary

network infrastructure; it should utilize existing GPRS mechanisms to provide an additional

layer of protection.

In the following section, we propose a novel defense mechanism implemented at the

GGSN,Adaptive PDP Context Management(APM) designed to detect and mitigate previously

mentioned attacks.

Motivation

Firewalls and IDSs are common mechanisms for defending against malicious behavior

from the Internet, but they have several disadvantages: (1)firewalls and IDSs become the single

point of failure, (2) they are external entities, and they usually do not protect against insider attacks,

(3) they are not flexible enough to dynamically adapt to traffic conditions without system adminis-

trators – they require knowledgeable administration staff, (4) they are not suitable for monitoring

peer-to-peer (such as Bluetooth) communication, and (5) they cannot protect against attacks exploit-

ing insecure protocols whose action is seemingly valid – they either allow or deny a connection.

Our defense framework attempts to avoid these downfalls of external firewalls and IDSs

by supplementing these protection mechanisms in order to detect and mitigate attacks that could


stealthily bypass firewalls and IDSs. Our defense mechanismcan also serve as an event detector for

IDSs already in place in order to monitor the internal network. Our defense mechanism is also effec-

tive against insider attacks, where malicious users are connected using the cellular network instead

of the Internet. Finally, APM is non-intrusive – it does not require ancillary network infrastructure

as it utilizes existing GPRS mechanisms to provide an additional layer of protection.

Using these two observations, we developed APM to detect andmitigate attacks on the

GGSN. APM, not only can completely mitigate our battery draining attack, but also detect and

mitigate other attacks exploiting the paging channel and PDP context, such as flooding attacks on

the paging channel using packets from the Internet.

Design Principle

We designed APM with three goals in mind,

• It should be implemented in the network core.

• It should be transparent to mobile users.

• It must be simple.

Since our attack focuses on draining the battery of mobile users, the defense strategy

should not exacerbate the attack by requiring additional processing from the mobile phone. If

this was not the case, the defense mechanism itself could be utilized as a battery draining tool.

Since the network core is assumed to have unlimited battery power, we must implement the defense

mechanism at the core. Furthermore, it is almost impossibleto implement any defense strategies

on the mobile phone since cellular technology has already been widely deployed. Service providers

cannot require all users to upgrade or update their hardware. Any type of defense strategy would be

useless if users do not implement the mechanism. For instance, software patches are often useless

against malware due to deployment issues. On the other hand,cellular providers can easily deploy

defense strategies at the core, without user interaction.

Our defense should also be transparent to each user. If our defense mechanism causes

any inconvenient for mobile user, user will most likely complain to service providers. Usability is

a main concern for mobile users since attacks on mobile phones are, at this time, unlikely and not

wide spread. Furthermore, service providers will be less inclined to implement our strategy due to

the inconvenience for users and the support cost to educate customers.


Outgoing

Packet

Yes

New Connection

No

PDP Context Exists

No

Drop Packet

Yes

No

Transmit Packet

Yes

stateCount * 2

Existing connection

Yes

Transmit Packet

No

stateCount > 0 Yes

No

(1) Backoff (2) PDP Modification (3) Drop Packet

(1) stateCount– (2) Transmit Packet

stateCount < max

Yes

Connection close

No

stateCount / 2

Yes

Figure 3.6: Adaptive PDP Context management scheme

Finally, our defense strategy should be as simple as possible due to the high workload

of each GGSN. GGSN is responsible for providing an interfacefor millions of mobile phones. If

our mechanism is computationally consuming, the attacker can exploit this vulnerability and in turn

cause a DoS on the GGSN.

Strategy overview

For clearity, we present APM in both pseudo code shown below,and state diagram shown

in Figure 3.6. APM is separated into three phases, the detection phase, exponential increase linear

decrease (EILD) phase, and the recovery phase.

APM(packet)

1 if packet is outgoing

2 then if packet initiates a new connection

3 then if statecount< statecountmax

4 then statecount∗2

5 if packet ends a connection

6 then statecount/2

7 else if PDP context exist

8 then if packet does not belongs to existing connection


9 then if statecount> 0

10 then statecount−1

11 else backoff

12 perform PDP Modification

13 drop packet

14

15 else drop packet

The APM detection algorithm works in the following manner. For each packet, the algo-

rithm decides if it’s valid or not. The GGSN can accomplish this by using our second observation

discussed in Section 5.1, a packet is not valid if it is incoming, and does not belong to any active

connections. Since GGSN is stateful, and already keeps track of connection states, it can distinguish

if a packet is valid or not by simply examining the header of each packet. However, to offload work

from the GGSN, we also propose a modification to the PDP context. Currently, PDP context only

contains the external address of each mobile device. Instead of simply storing the address, we can

store(IPaddress, portnumber) tuple. A modified PDP context can have multiple address and port

tuples. Whenever a mobile agent requests an outgoing connection, a tuple is assigned to it instead

of just an address. Using this technique, we can easily distinguish between valid and non-valid

incoming packets.

To manage PDP context lifetime, we introduce a new variable along with the PDP context

calledstateCount. This counter serves as the time to live (TTL) time for each PDP context. The

algorithm uses thestateCountvariable in the following way: when GGSN receives an outgoing

connection request or packet, a new tuple is assigned to the mobile phone, and thestateCountis

doubled. IfstateCountis 0, then we initialize it to 1. However, if GGSN receives an incoming

non-valid packet, thestateCountis decremented by 1. WhenstateCountdecreases to 0, GGSN can

conclude that the phone is under attack and perform recovery. This phase is calledexponential in-

crease, linear decrease(EILD). By implementing EILD, our algorithm can withstand some amounts

of false positive readings before raising an alert and entering the recovery phase. For example, it

would be hard to distinguish between valid and malicous streaming traffic. Furthermore, many port

scanners, worms, and other backscatter activities [50] areunavoidable on the Internet. Using EILD,

we can avoid disrupting the user as much as possible before weenter the recovery phase. Finally,

we note that PDP context can still be kept indefinitely, depending on service provider’s policy, as

long as the mobile agent is not under attack.


The recovery phase is implemented when thestateCountdecrements to 0. The recovery

phase is implemented as follows: before disconnecting the user, we implement a random backoff

wait period between (Cmin,Cmax). The wait period allows any existing connection to finish. After a

random backoff waiting period, GGSN implements a gateway assisted PDP context modification,

changing the external address in the PDP context. At this time, all connections currently still active

will be dropped, thus preventing the attacker from reachingthe mobile agent. Furthermore, only

one extra message would be sent to the mobile agent notifyingthe modification, and one extra

message would be sent from the mobile agent acknowledging the change. The mobile agent, after

the recovery phase, can resume data connection and request outgoing connections as usual.

Specification Modification

Our defense strategy can be safely implemented in existing GPRS infrastructure without

any violation to the specification [51]. In particular, GPRSspecification states that user should

be able to establish and deactivate GPRS service as requested. Our defense mechanism does not

violate any of the specification stated.

The specification does not clearly state any PDP context management schemes. In fact, the

specification does not restrict when PDP context should be deactivated. However, the specification,

under invocation and operation, states that,

It shall be possible for a MS to be a GPRS service requester andservice receiver.

Our defense mechanism would violate this specification. However, we argue that cellu-

lar devices should not act as a server or any service receiver. In fact, most service providers in

the US restricts mobile agent’s usage and does not allow any type of services to be active on any

mobile users. Furthermore, the specification allows our battery draining attack, and many other

attacks possible since it is allowed for an entity to activate the PDP context and communicate with

mobile devices. We argue that such action should not be encouraged and protection against such

exploitation should be straightly enforced.

Analytical Analysis

We now present an analytical analysis of our proposed defense strategy and provide a

simplistic calculation of the maximumstateCountvalue which must be set in order for our defense

to detect an attack.


We define the number of packets needed in order to mount a battery draining attack as

follows:

n×60secmin

×60mins

hr×hhours= 602nh. (3.1)

Given n = #packetss andh, the number of hours required to drain a phone’s battery (h ∼ 1

n) we can

calculate the upper bound on the number of outgoing connections that a cellular operator may set.

Parametersn and h are network dependent so each operator would have to tailor them to their

network.

In order to detect this attack, ourstateCountvariable must not exceed 602nh. Since we

exponentially increasestateCountin the fashion of 2connectionCount, we calculateconnectionCountmax

as follows:

connectionCountmax = ⌊log2602nh⌋ (3.2)

And following our argument from above, theconnectionCountshould be calculated as

follows:

connectionCount≤ ⌊log2602nh⌋ (3.3)

For example, forn= 1packets andh∼ 4 hours we notice thatconnectionCount= ⌊log214400⌋=

13. This means that the maximum number of connections each phone can make simultaneously in

order to detect an attack that sends1packets is 13 connections.

Note that this calculation provides a maximum for the connectionCount variable. Providers

should set this variable limit to a much smaller number, in order to detect any type of attack much

faster than this rate.

Implementation Details

As mentioned previously, our defense strategy is best implemented on the GGSN. Since

service providers already perform some proprietary PDP management scheme, as tested empirically3, implementing our scheme would be very simple. Furthermore, as most of the functions needed

3During our battery draining experiments, the PDP context sometimes would detach even if the mobile phone isstationary. We notice that PDP context can be alive from 15 minutes to even days.


are already implemented, such as the gateway assisted PDP context modification function, there

would not be any additional implementation work.

Furthermore, our proposed extension on the PDP context would also be a simple modifi-

cation. The implementation of the modified PDP context can betransparent to mobile devices, and

the mapping can be done entirely at the GGSN. Since GGSNs are already stateful, a simple change

in IP address assignment would not be difficult. Furthermore, our proposed modification to the PDP

context would also provide a NAT like behavior, as each IP address can be assigned multiple times

using different ports.

We envision APM to be implemented as a ”plug-in” module, which should not be any

longer than a couple of hundred lines of code. Since GGSNs arestandardized within each service

provider, a patch-like distribution can be easily deployedonce the module has been fully tested on

testbeds.

3.4 Conclusion

In this chapter, we demonstrated an attack, such that is ableto drain mobile devices’

battery power as much as 22 times faster. This attack proceeds in two stages. First, the attack

exploits vulnerabilities in MMS to build a hit list of mobiledevices. Then, the attack exploits PDP

content retention and the paging channel to drain mobile devices’ battery power. We were able to

drain batteries without alerting either the mobile user victims or the cellular network operators. Our

analysis shows that an attacker would need only several homeDSL Internet connections to mount

a large scale attack against a large number of cellular phones. We identified key components in

cellular networks that enable this attack and proposed corresponding mitigating solutions.

CHAPTER 4. SCHEDULER ATTACK 27

Chapter 4

Exploiting Opportunistic Scheduling in

Cellular Packet Networks

In this chapter, we study 3G and 3.5G networks and investigate the unwarranted trust

granted to mobile devices and the ensuing vulnerabilities.These networks rely on schedulers to

multiplex the spectrum efficiently. A commonly used scheduling algorithm is Proportional Fair

(PF) [52, 53], which maximizes the product of the throughputdelivered to all users. In this paper,

we reveal vulnerabilities in the PF scheduler and demonstrate that malicious mobile devices can

usurp time slots at the expense of honest users, hence denying them network access. For example,

we show that only one attacker per cell that has 50 users can occupy as much as 89% of the all the

scheduling slots indefinitely. Similarly, five attackers per cell can cause and perpetuate 2.1s end-to-

end inter-packet transmission delay for every victim user in the cell, thus rendering many services

useless.

4.1 Attack overview

Our attacks exploit vulnerabilities that result from unwarranted trust that the network

grants to mobile devices. By reporting false channel conditions and initiating frequent handoffs,

attackers can usurp the majority of downlink1 scheduling slots, causing intolerable delays to the

victim users and rendering many network services virtuallyuseless. We will give an overview of

the vulnerable 3G data network technologies and our attacks.

1From the network to the mobile users.


4.1.1 3G data networks

Cellular providers have developed two new data services, EV-DO and HSDPA, to pro-

vide broadband like downlink speed for emerging applications such as Voice-over-IP (VoIP), and

streaming video and audio, without major network restructuring. In both services, the downlink

utilizes time division multiplexing by dividing the channel in time slots, or Transmission Time In-

terval (TTI). (Note thatTTI = 1.67msfor EV-DO andTTI = 2msfor HSDPA). Theschedulerat

each base station then selects a single user to transmit at each TTI. Both services rely on two main

techniques to increase efficiency in the downlink direction: link adaptationandfast retransmission.

Link adaptation utilizes base station’s processing power to collect quasi instantaneous downlink

quality information — achannel quality indicator(CQI). Based on this CQI, the base station can

adapt data rate based on channel conditions: the better the channel condition, the higher the data

rate. Fast retransmission mechanism enables a mobile device to NACK each erroneous downlink

packet in order to request a retransmission from its base station instead of the originating server.

Opportunistic scheduling

Most 3G data services implement anopportunistic scheduler(Both HSDPA [54] and

EVDO [55] outline the use of an opportunistic scheduler in the downlink. Several service providers

also confirmed the use of opportunistic scheduler in their data networks) . In cellular networks,

channel conditions of mobile devices are time-varying and location-dependent. Since instantaneous

channel conditions derive the instantaneous data rates of mobile devices [56], mobile devices pe-

riodically measure and report their CQIs to their base stations. An opportunistic scheduler at a

base station selects a user with relatively good channel condition to transmit while maintaining pre-

defined QoS or fairness constraints. Thus, opportunistic schedulers often achieve higher network

performance than schedulers that do not take into account instantaneous channel conditions such as

round robin. A very popular opportunistic scheduler is Proportional Fair (PF), whose design goal is

to maximize the product of the throughput delivered to all users [52,53].

In PF, each mobile device measures its instantaneous channel conditions through pilot

signals, estimates the achievable data rate under its channel condition (denoted asCQIi(t) for user

i at timet), and sends the information back to the base station. To achieve the goal of maximizing

the product of the throughput delivered to all users [57], the PF scheduler chooses the user with

the highest ratio ofCQIi(t)/Ri(t) 2, whereRi(t) is the average throughput of useri at timet. It is

2PF makes scheduling decisions based on the ratioDRCi(t)/Ri(t) whereDRCi(t) = min{CQIk[n],Bk[n]tTTI

} andBk[n] is


estimated by the base station as follows:

Ri(t) =

αCQIi(t)+ (1−α)Ri(t −1) if the useri is scheduled att

(1−α)Ri(t −1) otherwise(4.1)

whereα is a network provider’s parameter describing the weight of the current time slot toward the

average. Typically,α is set as 0.001.

Handoff

Cellular networks implementhandoffsto transfer a connection from one base station to

another. There are two types of handoffs: soft and hard. In hard handoff, the network drops the

connection to the current base station before initiating a new one. In soft handoff, on the other

hand, a mobile device can have connections from several basestations simultaneously and choose

to transmit through the best base station. Noticeably, handoffs in 3G cellular services do not break

data transmission sessions.

4.1.2 Overview of attacks

3G data networks include mobile devices in their TCB. However, attackers can modify

mobile devices to perform actions different from intended by the providers, even when providers

attempt tamper-proof techniques [32, 58]. By trusting all mobile devices, 3G data networks suffer

from at least two vulnerabilities.

Fabricated CQIs Opportunistic schedulers base their scheduling decisionson CQIs reported by

mobile devices without verification. By reporting fabricated CQIs, malicious mobile devices can

manipulate the schedulers to achieve unfair network utilization and to disrupt other mobile devices.

For instance, a malicious mobile device can report an inflated CQI such that its ratio of CQI to

average data rate is the highest among all the devices in its cell, therefore ensuring that it will be

scheduled in the next time slot. By repeating this strategy,the malicious device may obtain a large

portion of slots in a short period of time, which causes largedelay and delay jitter to other users

(Section 4.2.1).

Greedy handoffs Mobile devices may initiate soft handoffs, but opportunistic schedulers are

oblivious of handoffs. For example, when a mobile device performs a handoff to another base

the buffer size. We eliminated buffer dependence from our calculations for simplicity.


station, the new base station does not retrieve the device’saverage data rate from its previous base

station [40], but rather assigns an often small or average value as the device’s initial average rate.

In the previous attack via reporting fabricated CQIs, the malicious mobile device has to report

monotonically increasing CQIs to sustain the attack because its average data rate keeps increasing.

Eventually, the attack becomes ineffective when its reported CQI exceeds the maximum allowable

CQI. However, if the malicious device sits in the coverage ofmultiple base stations, it may handoff

to another cell to acquire a fresh, lower average data rate and to start the attack again. Moreover,

multiple malicious devices may cooperate to attack multiple cells simultaneously (Section 4.2.2).

4.2 Attack analysis

Threat model Our threat model assumes that (1) attackers control one or a few mobile devices

that a cellular network has admitted; and (2) attackers havemodified the devices to report any CQI

value to the base station and to initiate handoff at any time.We believe this threat model is realistic.

Attackers can buy network-approved mobile devices and prepaid data plans or can spread worms

to take over existing mobile devices. Moreover, experiences show that attackers can modify mobile

devices to perform different actions than intended by the providers, even when providers attempt

temper-proof techniques [32, 58, 59]. However, our threat model does not assume that attackers

attack the cellular network infrastructure directly, e.g.by hacking into the network. Instead, they

exploit vulnerabilities in the network’s scheduler bymanipulating the information that their mobile

devices report to the network.

Attack settings From this point on, we useattackerto refer to either a human adversary or the

mobile device of the adversary (the context should differentiate the two meanings), and useuser

to refer to either a human user or the mobile device of the user. When an attack involves multiple

attackers, we assume that they coordinate. We will considerattacks on the proportional fair (PF)

scheduler under three settings. First, we consider attacksfrom a single cell, with a single or multiple

attackers. Next, we consider attacks from multiple cells, which is much more effective. Finally, we

consider a more realistic situation where the attackers do not know the channel conditions of other

users.


4.2.1 Attack within a single cell

We consider the situation when all the attackers stay in the same cell. Starting with one

attacker, we use mathematical analysis and simulation to evaluate his attack strategy. Then, we

extend our analysis to multiple attackers in the same cell. We assume that no user leaves or joins

the cell during the attack. Although this assumption is not crucial to our attack, it simplifies our

analysis. We also assume that the attackers know the channelconditions of all the users in the cell.

Section 4.2.3 will describe an attack strategy for the situations when this assumption does not hold.

Single attacker

The goal of the single attacker is to obtain a large number of consecutive time slots, there-

fore causing severe delay and jitter for the other victim users in the same cell. Since the PF scheduler

assigns the next time slot to the user that has the highest ratio of instantaneous achievable data rate

(measured in CQI) to average throughput, the attacker can report a large enough CQI to obtain the

time slot. To obtain consecutive time slots, the attacker must report monotonically increasing CQIs

(because its average throughput is increasing while other users’ throughput is decreasing, according

to Equation 4.1) until its reported CQI exceeds the range of CQI values.

It is difficult to calculate the precise number of consecutive time slots that the attacker can

get, because the number depends on the channel conditions ofall the users in the cell. However, we

can estimate an upper bound of this number by considering a simplified situation where each user

has the same CQI.3 First, we calculate the average throughput of a user. LetRi(t) be the average

throughput of useri at time slott. Recall from Section 4.1.1 that

Ri(t) =

αCQIi(t)+ (1−α)Ri(t −1) if the useri is scheduled att

(1−α)Ri(t −1) otherwise(4.2)

Since we assume that each user has the same CQI, the PF scheduler becomes a round robin sched-

uler, where each user is scheduled once everyN slots (N is the number of users in the cell). For

example, if useri is scheduled at time slots, he will not be scheduled until time slots+N. There-

fore, useri’s average rateRi(t) maximizes at time slots, and minimizes at the time slots+ N−1.

According to Equation 4.1,

Ri(s) = (1−α)NRi(s−N)+ αCQI (4.3)

3And each user always has outstanding data to receive.


Let us consider a steady state, whereRi(t) = Ri(t + kN) for all integerk. In this case,Ri(s) =

Ri(s−N). Using this equality in Equation 4.3, we have

Ri(s) =αCQI

1− (1−α)N ≈CQI

N(4.4)

Ri(s) is useri’s maximum throughput. His minimum throughput is

Ri(s−1) = Ri(s+N−1) = (1−α)N−1∗Ri(s) ≈ (1−α)N−1CQIN

(4.5)

Let C(t) = maxi{CQI/Ri(t)} be the maximum of CQI-to-throughput ratio at timet among all the

users. In the steady state,C(t) becomes a constantC, which is:

C =CQI

Ri(s−1)≈

N(1−α)N−1 (4.6)

Next, we describe a strategy for the attacker to obtain consecutive time slots. To obtain

time slot 1, the attackeri must report aCQIi(1) such thatCQIi(1)/Ri(0) ≥ C(0). After time slot

1, C(1) = C(0)/(1−α), because for each victim userj, its CQI remains constant, but its average

throughputRj has been scaled by 1−α. Therefore, to obtain time slot 2, the attackeri must report

CQIi(2) such thatCQIi(2)/Ri(1) ≥C(1) =C(0)/(1−α). Subsequently, at timet, the attacker must

claimCQIi(t) such thatCQIi(t)/Ri(t −1) ≥C(0)/(1−α)t−1. The attacker can obtain consecutive

time slots until the requiredCQIi(t) exceedsCQImax, the maximum value ofCQI. Therefore, the

maximum number of consecutive time slots that the attacker can obtain is the maximum integert0

that satisfies

CQImax≥C

(1−α)t0−1Ra(0)t0−1

∏k=1

(

αC(1−α)k−1 +(1−α)

)

(4.7)

Equation (4.7) shows that the maximum number of consecutiveslots an attacker can ob-

tain (t0) depends on the average throughput of the attacker at the beginning of the attack (Ri(0)),

the maximum CQI (CQImax), andα. Since the maximum CQI andα are set by the system, they

are out of the control of the attacker. The maximum CQI depends on the hardware.α is used to

balance the tradeoff between long-term and short-term performance. The smaller the valueα, the

better the system’s long-term throughput; however, when under attack, the smaller the valueα, the

larger the value oft0, i.e., the attacker can obtain more time slots. By comparison, the attacker has

control overRi(0), its average throughput at the beginning of the attack. Equation (4.7) shows that

the smaller the valueRa(0), the larger the valuet0. Therefore, after each attack session, the attacker

needs toresetits Ra(0) by reporting lowest CQI values for a sufficient period (typically on the order


of seconds). Finally, this model is simplified, assuming allvictim users have the same, consistent

CQI. When users have users have time-varying channel conditions, Equation 4.7 provides an upper

bound for estimatingt0.

Multiple attackers

A single attacker can obtain consecutive time slots until his reported CQI exceeds the

maximum CQI value; however, we can increase the number of consecutive time slots obtained by

using multiple colluding attackers. We describe three different coordinating schemes.

Sequential attack The simplest scheme is to attack sequentially. The attackerwith the smallest

average throughputRi(t) starts the attack and tries to obtain as many consecutive time slots as pos-

sible, while the other attackers lurk (by reporting arbitrarily small CQIs to avoid being scheduled).

When the active attacker’s reported CQI exceeds the maximumvalue of CQI, it stops the attack

while the attacker with the smallest average throughput starts to attack. The attack continues until

no attacker can get scheduled (because their average throughput is too high).

Minimum CQI Attack Since the attack will stop when all attackers’ reported CQIsexceed the

maximum value, this scheme tries to slow the increment of thereported CQIs. At each time slot,

each attacker computes the CQI that it needs obtain the time slot. Then, the attacker with the

smallest CQI reports its CQI to the base station while the other attackers lurk.

Delta CQI Attack This algorithm tries to slow the increment of calculated CQIvalues. At each

time slott, each attackeri computes the incrementδi(t) needed to its previous CQI. In other words,

δi(t) = CQIi(t)−CQIi(t −1). The attacker with the smallestδi(t) then reports its CQI to the base

station.

Simulation

We used simulation to evaluate the effectiveness of our attacks in a single cell. In the

simulation, we chose parameters that were recommended by specifications or that were commonly

used by cellular networks. The PF scheduler hadα = .001. The cell had 50 users. Each user

quantized his channel condition into CQI, an integer between 1 and 15, and reported the CQI to the

base station. The goal of the attack was to obtain the maximumnumber of consecutive time slots.


First, we simulated a single attacker in a cell with 49 victimusers. We used the same

ideal scenario as in our analysis in Section 4.2.1, i.e., allvictim users had the same CQI value. The

simulation showed that the attacker could obtain 42 consecutive time slots, whereas Equation 4.7

predicts that the attacker can obtain 39 consecutive time slots. The minor difference between the

simulation and the analysis is due to the approximation during the derivation of Equation 4.7.

Next, we simulated the same attack under a more realistic condition where each user’s

channel condition was a random variable following a Rayleigh distribution withσ = 3 and an initial

average rate of 0.5. The simulation showed that the attackergained an average of 19 time slots, with

a standard deviation of 2.77.

Next, we simulated multiple attackers in the same cell. Again, each user’s channel con-

dition was a random variable following a Rayleigh distribution. We varied the number of attackers

from one to five and simulated each of the attack schemes in Section 4.2.1. Figure 4.1 shows that

the number of collective consecutive time slots obtained bythe attackers increases almost linearly

with the number of attackers. Among the three attack schemes, the Delta CQI scheme performed

the best, where five attackers obtained 99 consecutive time slots.

0 1 2 3 4 50

20

40

60

80

100

Number of Attackers

Tim

eslo

t Occ

upie

d

DeltaMinimumSequential

Figure 4.1: Consecutive time slots obtained By attackers using different collaborating schemes in asingle cell.


Although 99 consecutive time slots (or 165ms) occupied by the attackers will cause delay

on victim users, this delay is tolerable by many applications and protocols. Moreover, after the

attack, the attackers must relinquish a large number of (at least 2000) time slots toresettheir average

throughput low enough before they can attack again. Therefore, the attackers cannot sustain this

delay. Fortunately (or unfortunately, depending on your stand), we were able to exploit another

vulnerability to make our attack much more effective and sustainable.

4.2.2 Attack from two cells

When attackers sit in the overlapping area of two cells, theycan exploit handoff to make

their attack much more effective and sustainable. Section 4.2.1 shows that an attacker’s reported

CQI and average throughput increase very fast during an attack. When a large average throughput

forces the attacker to report a CQI larger than its maximum value, the attack stops. However, since

users can initiate soft handoff and the network does not carry users’ average throughput across

cells, the attacker can hand off to another other cell, get a small initial average throughput, and

immediately start the attack in its new cell.

Initial average throughput

Since the network does not track users’ average throughput across cells [40], when a new

user joins a cell, the scheduler must first assign the user an initial value for its average throughput.

Since the choice of this initial value is unspecified, we explore three reasonable schemes. Although

these schemes are not all-inclusive, they represent good schemes that lead to predictable behavior

of the PF scheduler.

Based on the average of average throughput of all usersA simple scheme is to choose the

average of average throughput of all existing users in this cell as the initial average throughput of

the new user. The motivation for this scheme is the assumption that the new user’s channel condition

is close to the average channel condition of all existing users. The disadvantage of this scheme is

that when the new user just moves into his current cell from a neighboring cell, he is likely at the

edge of his current cell with poor channel condition, so thisscheme would over-estimate the new

user’s average throughput.


Based on the minimum of average throughput of all users Since new users often join a cell

from the edge of the cell, they expect to have the poorest channel condition. Therefore, this scheme

chooses the minimum of the average throughput of all existing users as the initial average throughput

of the new user. However, if the new user happens to have good channel condition, this scheme

would under-estimate the user’s average throughput.

Determined by the user Finally, since users are burdened with tasks such as channelquality and

pilot measurements for multiple cells, an intuitive schemeis to let users report their initial average

throughput. A major problem with this scheme is that the basestation trusts users blindly. An

attacker can report a bogus low average throughput to gain unfair advantage in scheduling.

Simulations

We simulated the attack from two cells. We used the same PF scheduler and the same

Raleigh distribution for users’ channel conditions as in Section 4.2.1. We simulated various number

of attackers per cell, from one to five. The attackers used thesequential attack algorithm described

in Section 4.2.1. However, after the last attacker in the cell finished his attack (i.e., when he could

obtain no more time slots), all the attackers in both cells handed off to the other cell. Although the

sequential attack algorithm is not the most effective, it isthe simplest and illustrates the lower bound

of the attack effect. We assume that handoff takes one time slot, which is realistic for soft handoff.

We ran the simulation for 18072 time slots, or 30 seconds. Figure 4.2 shows the per-

centage of time slots that the attackers got where there was one attacker per cell and the attackers

determined their initial throughput. It shows that after about 2000 time slots, the attackers consis-

tently obtained about 78% of all the time slots, a condition that we call the stabilization of the attack.

We simulated different number of attackers per cell and different schemes for assigning the initial

average throughput, and in all the simulations the attack stabilized well before 30 seconds.

Figure 4.3 shows the total number of time slots that the attackers obtained in 30 seconds.

Unsurprisingly, the more attackers per cell, the more time slots that they obtained. However, even

with just one attacker per cell, the attackers obtained from13459 (74%) to 16241 (90%) time slots,

depending on the scheme by which the scheduler assigns the initial average throughput. Among the

three schemes, the scheme that let the user provide this initial value is the most vulnerable, where

one attacker obtained 16241 (90%) time slots while five attackers obtained 17317 (96%) time slots.


0 5000 10000 150000.5

0.6

0.7

0.8

0.9

1

Time (timeslot)

% o

f Tim

eslo

ts o

btai

ned

Figure 4.2: Percentage of time slots obtained by two attackers, one per cell

4.2.3 Attack without knowing victims’ CQIs

So far, our attack requires the attackers to know all users’ channel conditions and average

throughput at each time slot. In practice, however, attackers may not have such information. In this

case, the attacker must predict the maximum of the CQI-to-throughput ratios of all the victim users,

because the attacker can obtain the next time slot only if hisown ratio is larger than those of all the

victim users. We show how the attacker can predict the value.

First, the attacker performs a statistical analysis of the PF scheduler and estimates a dis-

tribution of the max ratio of all users, shown in Figure 4.4. Based on this distribution, the attacker

determines the CQI that he needs to obtain the next time slot with high probability. The higher CQI

that the attacker reports, the higher probability that he will obtain the next time slot; however, in this

case, he will also exceed the maximum CQI value more rapidly,which will force him to hand off

to another cell. Since the attacker cannot obtain time slotsduring handoff, he must strike a balance

between obtaining the next time slot with high probability (by reporting higher CQIs) and reducing

the frequency of handoffs (by reporting lower CQIs). In the simulation, we choose the 90% value

of thec(t) in Figure 4.4. In other words, the attacker has 90% chance of obtaining a time slot.

During the attack, the attacker must adjust the estimated maximum CQI-to-throughput


0 1 2 3 4 50

2000

4000

6000

8000

10000

12000

14000

16000

18000

Attackers per Cell

Tim

eslo

ts O

ccup

ied

User ProvidedMin. user avg.Mean user avg.

Figure 4.3: Time slots occupied by the attack in 30 seconds (18072 time slots). The attackers havefull knowledge of every user’s information in each cell. Thethree lines represent different schemesfor assigning the initial average throughput by the base station

ratio of all the victim users constantly. This is because that in every time slot, each user’s average

throughput will increase byα∗CQI if he is scheduled, and decrease by a factor of(1−α) otherwise.

We propose the following scheme for adjusting the maximum ratio estimation.

Let c(t) be the estimated maximum CQI-to-throughput ratio at timet, derived from the

distribution in Figure 4.4, andRi(t) be the average throughput of useri at timet. If the attacker is

scheduled in timet, the average throughput of all the other users will decrease, Ri(t) = (1−α) ∗

Ri(t −1). Sincec(t) estimates the largestRi(t) of all the victim users, it increases at the same rate,

c(t + 1) = c(t)/(1−α). When the attacker is not scheduled, on the other hand, only the average

rate of the victim user who is scheduled will increase. Therefore,

c(t +1) = maxi

CQIi(t +1)

Ri(t)≈ max

i

CQIi(t +1)

Ri(t −1)∗ (1−α)+ α/N∗CQIi(t)

= maxi

CQIi(t +1)/Ri(t −1)

(1−α)+ α/N∗CQIi(t)/Ri(t −1)≈

c(t)(1−α)+ α/N∗c(t)

Some approximations are involved in the above estimation. First, on average, a victim user

gets scheduled once everyN times when the attacker is not scheduled. Therefore, the average rate of

a victim user will be increased byα/N ∗CQIi(t) when the attacker is not scheduled approximately.


30 40 50 60 70 80 900

100

200

300

400

500

600

700

Scheduled CQI/R ratio

Num

ber

of ti

mes

sch

edul

ed

Figure 4.4: Histogram of the CQI-to-throughput ratio of thescheduled user over 10000 time slots

Second, when a user is scheduled, his CQI-to-throughput ratio is the maximum among all users and

thus its value ofCQIi(t)/Ri(t −1) is approximatelyc(t). Equation 4.8 summarizes our analysis:

c(t +1) =

c(t)/(1− ε) if the attacker is scheduled

c(t)/(1+ σ∗ (c(t)−1)) if the attacker is not scheduled(4.8)

whereε andσ are functions ofα. We usedε andσ instead ofα to compensate for the possible errors

in our estimation of the maximum CQI-to-throughput ratio, and we determined them empirically.

We reran the simulations in Section 4.2.2 but with the estimated maximum CQI-to-throughput

ratio using Equation 4.8. As we experimented with differentchoices ofε andσ, we noticed that

over-estimation of the maximum CQI-to-throughput ratio yielded better attack results than under-

estimation.

Figures 4.5(a) shows the number of time slots obtained by theattackers withε = 1.5∗α

andσ = α/60. When there is a single attacker per cell, the attackers may obtain between 11583

(64%) and 15874 (88%) time slots, depending on the scheme forassigning initial average through-

put. When there are five attackers per cell, they can obtain between 14353 (79%) and 17136 (95%)

time slots. Next, we compare the effect of the attack under this realistic situation where attacks do

not know the CQIs of the victims with the effect of the attack under the ideal situation. Figure 4.5(b)


shows the percentage of time slots that the attackers obtainwhen they do not know the CQIs of the

victim users, compared to the case with perfect information. If the PF scheduler uses user-provided

initial average throughput, the attackers, using our estimation, can obtain almost the same number

of time slots, as in the ideal situation when the attackers know the value ofc(t) perfectly. Even when

the PF scheduler uses the two other schemes, which are more robust against our attack, the attackers

could still obtain more than 85% of the time slots that they would obtain in the ideal situation.

4.3 Attack impact

In this section, we discuss the impact of our attacks on normal victim mobile users. We

first produce a benchmark from an honest user. Then, we recount a study on a particular application,

VoIP, and show how the attack can render VoIP useless.

Overall and individual average throughput Figure 4.6 compares the distribution of users’ through-

put in a cell before and during the attack described in Section 4.2.2. We calculate the throughput

based on HSDPA’s mapping of CQI to data rate [60, 61]. Before the attack, most users obtain an

average throughput of 40 to 55 kbps. During the attack, most users obtain an average throughput of

only 10 to 15 kbps. By comparison, the attacker obtains an unusually high average throughput of

1.5Mbps4 5. This figure shows that the attack has defeated the fairness objective of PF entirely.

Average user delay Figure 4.7 depictsthe average delay between each transmissionfor victim

users. In a normal application, users experience a slight delay of 0.081 seconds between each

transmission, which is acceptable in most applications. This number, by the way, would most likely

be different if QoS requirements are deployed. Note that PF scheduler functions very similar to a

round robin scheduler in this case, even with a random channel condition. For example, in a cell

with 50 users, each user waits around 49 timeslots for a transmission. The delay variance of a user

in a PF scheduler is higher (in particular,N2−N whereN is the number of users).

During the attack, on the other hand, a victim user can experience up to 1.8 seconds delay

(22 times the delay before the attack) between each pair of successive transmissions. This significant

delay will render many applications virtually useless.

4Not shown in graph due to space constraints5The reason why the attacker’s average throughput is not significantly higher is because of handoffs, which resets the

attacker’s average throughput.


Impacts on applications Cellular providers have already started offering the VoIP service due

to its lower cost. However, VoIP packets have a rigorous delay requirement: 0-150ms delay is

acceptable, 150ms-400ms delay might be tolerable, but longer delay is disruptive [62]. This delay

budget is for end-to-end delay, including coding/decodingtime (around 20ms), transmission delay

over the Internet (about 100ms across the continental USA) and uplink and downlink delay to the

user. Therefore, the delay on the cellular link is important.

Using VoIP as an example, we develop a function to calculate the end-to-end delay be-

tween two cellular users. Letf (DNW,N,U,X) be the end-to-end, one way delay between two

users, whereDNW is the backbone network delay,N is the number of users in a cell,U is the uplink

network delay, andX is the delay that the attack induces to the downlink. Equation 4.9 shows the

formula for EV-DO, based on Goode’s calculation [63].

f (DNW,N,U,X) = 101.1ms+DNWms+(N)(1.67ms)+Ums+Xms (4.9)

Recall that we have shown in Figure 4.7 that honest users can experience up to 1.8 seconds

of delay between each transmission. Using equation 4.9, we obtain 2094.6ms ofend-to-end delay

per packet for every honest user in the cell. This delay is devastating for VoIP communication

as it renders it useless. Furthermore, the attack can be extented indefinitely with only 5 attackers

comprising only 10% of total number of users in the cell.

4.4 Possible defense strategies

The above-illuminated attack exploits several vulnerabilities that in combination, enable

any user to perform a denial-of-service attack on downlink cellular data service. To defend against

this type of attacks, we must either eliminate these vulnerabilities or mediate them such that this type

of attack is no longer effective. Here we outline some defense strategies that could be implemented

either in future specification revisions or current implementations.

4.4.1 Attack detection

There are two characteristics that the base station can use to distinguish normal and under-

attack operations in the attack outlined in section 4.2; namely, decrease of average user throughput

andexcessive numbers of handoffs. We can take advantage of these characteristics in composing a

defense strategy.


Anomaly detection using average throughput in a normal condition The base station can mea-

sure an average user’s throughput during normal operation,either by simulation or by actual mea-

surement. Then, it can compare the current throughput with recorded normal throughput. If their

difference is above a certain threshold, this could indicate that the system is under attack. The base

station then can use several methods to mitigate the attack,including using a scheduler that does not

require user collaboration, such as round robin, and tracing the attack source.

Number of handoffs per user In a normal operation, users do not perform excessive handoffs.

On the other hand, attackers performs handoffs as many as oneevery 5 time slots. The base station

can observe the number of handoffs performed per user over a period of time. If a user performs

an unusually high number of handoffs in a given time, the basestation can reject further handoff

requests, thereby stopping the attack in that cell.

4.4.2 Attack prevention

In addition to attack detection, network designers can implement modifications to the

existing architecture to prevent the attack. We present some possible mitigation strategies that are

designed to stop this attack from causing significant damage.

Trust but verify The root of this attack is that the cellular network considers mobile users in

its TCB, and subsequently makes decisions based on unverified user input. One possible defense

strategy that the base station can perform is to periodically check the validity of the various reports

made by the mobile devices. This approach,trust, but verify, places a level of trust on a mobile

device as it passes random checks. The CQI reported by the mobile device can be randomly checked

using their uplink channel condition and error rate.6. Mobile station gains trust as it passes random

check, and loses trust when it fails. The base station can disconnect mobile users if they fail checks

for a number of times thereby punishing malicious or badly configured users.

Assume an average based on the normal operating conditionThe base station can define an

average such that it reflects the normal behavior of the cell,given the number of users in the cell.

This scheme punishes the attacker because he will be assigned a high average each time he switches

cells and thus force the attacker to perform handoffs much earlier. Figure 4.8 illustrates that this

6Estimating channel condition using uplink data rate is not perfectly accurate; however, it can still detect anomalousclaims


scheme is much more resilient to attack than the strategy that solely relies on the cell’s current

information. This scheme does not require a change to the current architecture nor does it require

collaboration between network entities.

This scheme is not without cost. During handoff, an honest user is usually at the edge of

the cell with relatively poor channel condition. Assigninga fixed average may starve the user for a

while. Additionally, while this scheme alleviates the effect of attacks by forcing more handoffs, it

does not solve the whole problem.

Scheduler information sharing The attack discussed in Section 4.2.2 accentuates the fact that

PF is oblivious of handoffs. When a mobile device performs a handoff the base station must derive

the mobile users’ average throughput using information confined to the current cell. Therefore,

whenever the attacker performs a handoff, the attacker’s excessively high average throughput gets

replaced with a new, calculated average throughput from thenew base station.

The attack can be stopped if base stations communicate and transfer users’ average through-

put along with a mobile device during handoff. In this case, the attacker cannot continue the attack

for more than a few slots since the average throughput is extremely high. This defense mechanism

can limit the effect of the attack to that in a single cell and therefore reduce the problem to service

degradation instead of denial of service. Figure 4.8 shows the performance of this defense strat-

egy compared to the average throughput assignment strategy, which does not require collaboration.

Notice that by sharing information between cells, the attack can be significantly deterred.

Modified schedulers with multi-dimensional constraints Another possible defense strategy is

to introduce additional constraints into the scheduler. These temporal constraints limits the portion

of time slots allocated to the attackers, stopping the attack.

The base station can implement two types of temporal constraints, either individually, or

combined. Thelong-term temporal constraintlimits the minimum and (possibly) maximum portion

of time slots allocated to each user over a long period of time(usually during the lifetime of a user,

on the order of minutes). Theshort-term temporal constraintguarantees that each user obtains a

minimum (and possibly maximum) number of time slots within atime window, on the order of a

few hundred milliseconds.

Long-term temporal constraints can be easily satisfied due to the PF scheduler’s fairness

constraints in a normal operation. The impact of the short-term constraint, on the other hand, de-

pends on the parameters used. The defense capability becomes more effective as the number of slots


assigned to each user in a short-term increases, but lowers overall throughput7. However, short-

term constraint is also useful in improving short-term fairness among users and reduce throughput

burstiness, which is desirable to upper layer protocols, such as TCP.

Priority queue Additionally, a priority queue can be implemented at the base station. Traffic

with delay constraints, such as VoIP traffic, can be scheduled with high priority, while other traffic,

such as web browsing, can be scheduled with low priority. While an attacker can claim to be

high priority, because the number of high priority users is relatively small, these users have much

better delay performance, and thus mitigate the effects of the attacks (in particular, attacks without

handoff). In addition, the priority scheme may be combined with temporal constraints so that an

attacker cannot claim a large portion of resource (for attacks with handoff).

4.5 Conclusion

In this chapter, we have shown that cellular data networks are vulnerable to DoS attacks

because of the following vulnerabilities:

• The network trusts mobile devices to report truthful CQIs, which the PF scheduler uses with-

out verification for assigning time slots. Therefore, malicious mobile devices can manipulate

their reported CQIs to gain a large number of time slots.

• The network does not track the average throughput of mobile devices across different cells,

which allows malicious devices to maintain perpetual scheduling priority by frequent hand-

offs.

We have studied a series of attacks on the proportional fair scheduler exploiting the above

vulnerabilities. Our simulations show that just one attacker per cell can disrupt time-sensitive data

services, such as voice-over-IP. Moreover, multiple attackers in the same cell can collaborate to

cause serious denial of service by occupying up to 95% of scheduling slots indefinitely. Meanwhile,

they can also induce 1.8s delay between each consecutive packet transmission on every victim user

who is in the same cell as the attackers. We have proposed several mitigation strategies to defend

against these attacks.

7because the scheduler has to schedule users given the short-term constraint although there might be a user with ahigher value ofCQI/R


0 1 2 3 4 50

2000

4000

6000

8000

10000

12000

14000

16000

18000

Attackers per Cell

Tim

eslo

ts C

ccup

ied

User providedMin. user avg.Mean user avg.

(a) Time slots obtained by the attack in 30 seconds (18072 slots). The attackers do not have

any knowledge of victims’ CQIs.

0 1 2 3 4 50

20

40

60

80

100

Attackers per Cell

Acc

urac

y of

Pre

dict

ion

(%)

User ProvidedMin. user Avg.Mean user Avg.

(b) Percentage of time slots obtained by attackers without knowing victims’ CQIs compared

to those with knowing victims’ CQIs

Figure 4.5: Performance of the attack without knowing victims’ CQIs. Each sub-figure shows threecurves, each representing a different scheme for assigningthe initial average throughput.


0 10 20 30 40 50 600

5

10

15

20

25

Average Throughput (kbps)

% o

f Use

rs

After Attack

Figure 4.6: The average throughput distribution of users, before and after attack.


0 1 2 3 4 50

0.5

1

1.5

2

2.5

Attackers per Cell

Vic

tim’s

Ave

rage

Del

ay (

s)

Full KnowledgePrediction

Figure 4.7: The average delay an honest user experiences between each packet transmission

.


0 1 2 3 4 50

2000

4000

6000

8000

10000

12000

14000

16000

18000

Attackers per Cell

Tim

eslo

ts O

ccup

ied

Min. User Avg.Mean Norm. User Avg.Collaboration

Figure 4.8: Continuous number of timeslots occupied by the attack using different defense strategiescompared to a normal attack in full knowledge

CHAPTER 5. SUMMARY AND CONCLUSION 49

Chapter 5

Summary and Conclusion

While cellular users embrace new broadband data services and applications, attackers get

opportunities to exploit emerging vulnerabilities in mobile devices, cellular data networks, and the

interaction between cellular networks and the Internet. Wedemonstrated two particular denial of

service attacks that can cause havoc in cellular network with relatively few resources.

In the first attack in Chapter 3, we demonstrated drains mobile devices’ battery power as

much as 22 times faster. This attack proceeds in two stages. First, the attack exploits vulnerabilities

in MMS to build a hit list of mobile devices. Then, the attack exploits PDP content retention and

the paging channel to drain mobile devices’ battery power. We were able to drain batteries without

alerting either the mobile user victims or the cellular network operators. Our analysis shows that

an attacker would need only several home DSL Internet connections to mount a large scale attack

against a large number of cellular phones. We identified key components in cellular networks that

enable this attack and proposed corresponding mitigating solutions.

In the second attack in Chapter 4, we studied a series of attacks on the proportional fair

scheduler exploiting the 3G and PF vulnerabilities. Our simulations show that just one attacker

per cell can disrupt time-sensitive data services, such as voice-over-IP. Moreover, multiple attackers

in the same cell can collaborate to cause serious denial of service by occupying up to 95% of

scheduling slots indefinitely. Meanwhile, they can also induce 1.8s delay between each consecutive

packet transmission on every victim user who is in the same cell as the attackers. We have proposed

several mitigation strategies to defend against these attacks.

Due to the complex interaction between mobile devices, cellular data networks, and the

Internet, we conjecture that our discovered attacks may be just the tip of an iceberg. We hope that

our work will bring attention to this emerging threat and will inspire future research for securing

CHAPTER 5. SUMMARY AND CONCLUSION 50

cellular data services and applications. Furthermore, we hope to motivate the cellular industry to

improve the security in their current data services, and to scrutinize the security in their future

specifications more rigorously.

BIBLIOGRAPHY 51

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Unique Vulnerabilities and Attacks on Cellular Data … · 2015-07-28 · formation security rather than network security. We argue that network vulnerabilities can cause We argue