Can You Infect Me Now?Malware Propagation

in Mobile Phone Networks

Authors:

Presented by: Michael Annichiarico

Mobile Malware

Like normal malware, but on mobile phones (smart phones and dumb ones too)

Why worry about mobile malware? “combination of vulnerable platforms

(symbian), unsuspecting users, and explosive growth in potential victims will inevitably attract propagating malware”

What Makes This Paper Different?

Previous malware propagation research: Proximity Propagation

Bluetooth, etc

This research: Focuses on propagation via the

telecommunications network

Why Moble Malware?(from the bad guy's perspective)

Smart phones are a lot like PCs: market share per OS (72% symbian) software vulnerabilities exist

Exploited smart phones could provide an attacker with means to: steal private data / users' identities spam make free calls execute (D)DoS

Main Paper Goal(s)

Simulate the effects of mobile malware propagation via the telecommunications network Simulated both VoIP malware and MMS

malware

Draw some conclusions for defending

Simulator

Event Driven, Custom Code. (so they could better adapt for their needs)

1 second step size, stepping 12 hours Infection beginning at a single phone Telecom Network

UMTS Topology

Boston Metro Area

Network: UMTS

UMTS is the 3G successor to GSM (2.5G/GPRS, 2.75G/EDGE) Network side is very similar to GSM, air

interface side changed to support higher data rates.

Signaling and control are negligible (ignored in the model)

Topology: Boston Metro Area

100sq miles, divided into 1sq mile cells

Mobile Station Distribution from US Census data scaled by 78% (by cell phone penetration)

Mobility is not modeled Authors speculate the bottleneck will be in

the network, not at the air interface

Simplified UTMS Network

Simulation Construction

Assume normal MMS usage is based on a charge per message

MMS Server Capacity Server handles 100 msg/sec, although higher rates

were simulated with “a qualitatively similar result” Authors explanation: MMS server will not be dimensioned

to handle users behaving like an aggressive worm (i.e., sending large numbers of messages as quickly as possible).

Bottom-up design of the UMTS Network

Simplified UTMS Network

Simplified UTMS Network

Simplified UTMS Network

Simplified UTMS Network

Simplified UTMS Network

Simplified UTMS Network

Simplified UTMS Network

Modeled UTMS Network

Simulation Parameters

1 single serverserving 100 msg/sec

49 serversserving 10k users each

49 servers

9616 Node B's

2Mbps

100Mbps

1Gbps links between SGSNs

Simulation Notes

“The granularity of our Node B placement was a limiting factor of our initial population data. A finer granularity would, no doubt, offer a more detailed and accurate picture of malware propagation.”

Spreading via Phone books/Contact Lists

No published studies of address book characteristics found, so:

1-1000 contacts (upper limit from empirical data on phone book maximums)

Phone book/contact degree distributions based on statistical analysis

Phonebook/contact degree distributions(for contact list size)

Power-Law: from yahoo email groups, and other authors' research.

Log-Normal: from social networking websites' statistics.

Erlang Dist: from authors' experiment (but very small sample size of 73)

Node Attachment ... you dont call everybody in your address book

Probabilistically randomly assign address book size based on distribution, then...

70% - “The probability that two users were friends was proportional to the inverse of the number of people between them.”(from LiveJournal.com study)

30% uniformly randomly assigned

Attack Vector: VoIP

Assumes vulnerable service on the mobile phone which does not require user interaction

Assume all phones are vulnerable. (Authors note that in reality a fraction

would be vulnerable, and they state a qualitatively similar result)

Simulated Propagation of VoIP Malware

“...constrained bandwidth should also be considered; but doing so requires estimating typical traffic characteristics, and we lacked meaningful data on which to base such estimates.” --- ?????

Techniques for Faster Propagation of VoIP Malware (and Simulation Results)

Divide and distribute (transfer) contacts from address book

Congestion backoff (wait) 10s

Attack Vector: MMS

Handled by central MMS server

Requires user interaction only a percentage “F” act on message

Can be done while phone is off So there is a wait time to answer messages.

Mixture of two Gaussian distributions centered at 20s & 45m

Simulated Propagation of MMS Malware

Techniques for Faster Propagation of MMS Malware

Congestion backoff (10s) Not very much advantage, due to MMS central

server constraint.

Divide and distribute contacts from address book Same as above

Global contact book method Infected half the population in 12 hrs. (what F

value?)

Faster MMS Malware Propagation

Defending Against Mobile Malware Propagation in Telecom. Networks

(This section is way too small in the paper, would have liked to see more on this.)

Rate Limiting ACCELLERATES infection! (same as congestion

avoidance) Blacklisting Containment

large number still get infected more slowly (no details given on %).

removing phones leads to a less congested network for those infected but non-blacklisted phones

Content Filtering “Seems promising due to centralized topology.”

"Investigating whether it's practical remains future work." (and they didnt provide any information on how promising or why)

Questions?