Secure and Trustworthy Data Management for Vehicular Cyber Physical Systems Dr. Wenjia Li Assistant Professor in Computer Science New York Institute of

Secure and Trustworthy Data Secure and Trustworthy Data Management for Vehicular Cyber Management for Vehicular Cyber

Physical SystemsPhysical Systems

Dr. Wenjia LiAssistant Professor in Computer Science

New York Institute of Technology

04/18/23 1

AgendaAgenda

• Introduction and Motivation

• Prior Research Efforts

• The Proposed Approach

• Research Challenges/Opportunities

• Conclusion

04/18/23 2

VariousVarious Applications of Wireless Network and CPS Applications of Wireless Network and CPS

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Wireless Network

Emergency/Disaster Rescue

Intelligent Transportation

Situation Awareness for Battlefield

Mobile Healthcare System

ABCs of Wireless NetworksABCs of Wireless Networks• Wireless Network: a kind of computer network that offers

ubiquitous access for various devices (laptops, smart phones, tablets, sensors, RSUs, etc.)

• Basic features of wireless networks– Limited battery life of each device

• Ever complained about short battery life of your smart phone?

– Short, open & error-prone transmission medium• Don’t forget to encrypt your WiFi network

– Constantly changing network topology• Keep in mind devices (and cars which carry them) are always moving

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Cooperation among devices is very important for wireless networks Cooperation among devices is very important for wireless networks

What if Devices What if Devices DON’TDON’T Cooperate? Cooperate?

• Some nodes can exhibit uncooperative behaviors due to one of the following two reasons– Anomalies (such as device malfunctioning, power outage,

high wind, etc.)• These behaviors are classified as faulty behaviors

– Intentionally disturbing network and causing damage• These behaviors are known as malicious behaviors

• Both faulty behaviors and malicious behaviors are regarded as MISBEHAVIORS– Which type is MORE dangerous, malicious or faulty?

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Node MisbehaviorsNode Misbehaviors• Why we want to detect and fight

against node misbehaviors?– Minimize the harm they cause– Punish misbehaving nodes– Encourage node cooperation

Countermeasures are NEEDED to address the security threats led by various node misbehaviors, especially those malicious ones

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Watching Your NeighborsWatching Your Neighbors: Example: Example

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Observer

Observed Nodes

Incoming Incoming PacketPacketIncoming Incoming PacketPacket Incoming Incoming

PacketPacket AAIncoming Incoming PacketPacket AA

11 22

33

Outgoing Packet BOutgoing Packet B

1: Packet 1: Packet DroppedDropped

2: Packet 2: Packet ModifiedModified2: Packet 2: Packet ModifiedModified

3: DoS attack3: DoS attack

Radio

RangeRadio

Range

Sending MANY dummy data to occupy channelSending MANY dummy data to occupy channel

Traffic Monitoring – An ITS ApplicationTraffic Monitoring – An ITS Application

• Data security and trustworthiness are CRITICAL to the traffic monitoring application

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How to How to SecureSecure Vehicular CPS?Vehicular CPS?

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Misbehavior DetectionMisbehavior Detection

• An important method to protect wireless networks and CPS from BOTH external attackers AND internal compromised nodes

• Previous misbehavior detection methods– Intrusion detection system (IDS) for wireless networks

• IDS sensor deployed on each node– NOT energy-efficient

• Cluster-based IDS by Huang et al.

– Cross-layer misbehavior detection by Parker et al.– Efforts to identify routing misbehaviors

• “Watchdog” & “Pathrater” by Marti et al.

Trust ManagementTrust Management• Goal: assess various behaviors of other nodes and

build a trust for each node based on the behavior assessment

• Node behavior observation– First-hand observation

• Directly observed• Most trustworthy but only contains behaviors of DIRECT neighbors

– Second-hand observation• Exchanged with other nodes• Less trustworthy but contains behavior observations for all the nodes

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PreviousPrevious Research Efforts in Trust Research Efforts in Trust ManagementManagement

• Cooperation Of Nodes, Fairness In Dynamic Ad-hoc NeTworks (CONFIDANT) by Buchegger et al.– Aim: encourage the node cooperation and punish misbehaving nodes – Components: Monitor, Reputation System, Trust Manager, and Path Manager – Exchange both positive and negative observations with neighbors

• CORE by Michiardi et al. – Similar to CONFIDANT– ONLY exchange POSITIVE observation with neighbors

• Reputation system by Patwardhan et al.– Reputation determined by data validation– A few nodes named Anchor Nodes are trustworthy data sources– Data validation by either agreement among peers or direct communication

with an anchor node

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MotivationMotivation

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Wireless Network

Misbehavior Detection

Trust Management

Context Awareness

1

3

2

45

6

Node 1 is misbehaving

because it drops packets

Node 1 is NOT trustworthy


Nodes 2 and 4 (1’s neighbors)

are busy sending packets

TraditionalTraditional Security Solutions Security Solutions

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Q: Is Node 1 really malicious or not?

An Example ScenarioAn Example Scenario

• Can we survive at -173 oC ?– Probably NO!

• Error reading from sensor?– Maybe YES!

• Malicious or faulty?– Totally NO clue!

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Another Example ScenarioAnother Example Scenario

• Node 1 are Node 1 are equallyequally trustworthy in both cases? trustworthy in both cases?– Probably Probably YESYES according to traditional security mechanisms according to traditional security mechanisms– But actually But actually NONO because of the context in which the packet dropping because of the context in which the packet dropping

occurs!occurs!04/18/23 16

Our Solution – A Our Solution – A HolisticHolistic Framework Framework

• A holistic framework that integrates misbehavior detection, trust management, context awareness and policy management in a cooperative and adaptive manner– Misbehavior detection that does not rely on pre-defined fixed

threshold– Models node trust as a vector instead of a scalar in wireless

networks– Declares and enforces policies that better reflect the context

in which misbehaviors occur

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Why Our Solution is Better? – An ExampleWhy Our Solution is Better? – An Example

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Mobile Ad-hoc Network

Misbehavior Detection

Trust Management

Context Awareness

1

3

2

45

6

Data

Data

Data

Node 1 is misbehaving


Node 1 is NOT trustworthy


Nodes 2 and 4 (1’s neighbors)

are busy sending packets

Policy Management

Busy channel for node 1

Node 1 is forced to drop packets but it is NOT malicious

its trust gets punished less

A A CloserCloser Look at the Look at the Proposed SolutionsProposed Solutions

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How do How do TraditionalTraditional Misbehavior Misbehavior Detection Methods Work?Detection Methods Work?

• Threshold-based solution:– “If total bad behavior > 10, then the node is misbehaving.”

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Packet Drop Packet Modify Packet Flooding Total Bad Behavior

Node 1 18 4 8

Node 2 5 15 10

Node 3 4 10 16

Weight 0.1 0.4 0.5Weights sum up to 1

7.411.512.4

GOOD

BAD

• Challenges:– Both the weights and the threshold are hard to decide manually because

they heavily depend on environment and context!

Our Solution: Support Vector Machine (Our Solution: Support Vector Machine (SVMSVM))

• Support Vector Machine (SVM): a machine learning algorithm that is used to automatically classify nodes into misbehaving nodes and normal ones– SVM requires a set of training data to build the model

• Training stage:

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Packet Drop

Packet Modify

Packet Flooding

Bad Guy?

Node 1 18 4 8 No

Node 2 5 15 10 Yes

Node 3 4 10 16 Yes

…

SVM Algorithm

An SVM Model

Support Vector Machine: Detection StageSupport Vector Machine: Detection Stage

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• Detection stage:

The SVM Model

Packet Drop

Packet Modify

Packet Flooding

Bad Guy?

Node X 16 6 8 ?

Node Y 2 19 9 ?

Node Z 6 11 13 ?

Packet Drop

Packet Modify

Packet Flooding

Bad Guy?

Node X 16 6 8 No

Node Y 2 19 9 Yes

Node Z 6 11 13 Yes

Trust: A Scalar or A Vector?Trust: A Scalar or A Vector?

• Majority of current trust management schemes in wireless network model trust in ONE single scalar (i.e., one single value)– Observations to all types of misbehaviors are

used to determine ONE single trust value for each node

– Neither expressive nor accurate in complicated scenarios

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How did How did OthersOthers Evaluate Trust? Evaluate Trust?

Observer

04/18/23 24

10 10 Incoming Incoming PacketsPackets


1010 Incoming Incoming PacketsPackets AAii


11

22

33

10 Outgoing Packets Bi


Node 1: Node 1: 1010 Packets Packets

DroppedDropped


ModifiedModified


ModifiedModified

Ten Misused RTS requests


Node 3: 10 RTS flooding

attack


attack

Radio Range

Radio Range

Trust_1 =Trust_2 = Trust_3 =

0.9

Trust_1 =Trust_2 = Trust_3 =

0.9

OurOur Solution for Trust Management Solution for Trust Management

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Observer





11

22

33




DroppedDropped


ModifiedModified


ModifiedModified




attack


attack

Radio Range

Radio Range

T1 T2 T3

Node1 0.9 1 1

Node2 1 0.9 1

Node3 1 1 0.9

MultiMulti-dimensional Trust Management-dimensional Trust Management

• Multi-dimensional trust management– Decide the trustworthiness

of a node from several perspectives (for example 3)

– Each dimension of trustworthiness is decided by a subset of misbehaviors

04/18/23 26

Research Challenges/OpportunitiesResearch Challenges/Opportunities

• Short-term trust V.S. long-term trust (Data V.S. Device)– Sometimes you will NOT see your next car in highway again

(not for a long time or never)!– In many cases we are also (or MORE) interested in how

trustworthy a traffic event/alert is rather than the guy who reported it

– So we want to evaluate and track the trustworthiness of the traffic data!

04/18/23 27

Research Challenges/ Opportunities (Cont.)Research Challenges/ Opportunities (Cont.)

• Heterogeneous Sensor Data– Smartphone sensor data V.S. on-board vehicular

sensor data (and even more)– How can we properly interpret and integrate these

heterogeneous sensor data?– One solution: use policy rules as well as contextual

information to help fuse these sensor data to better utilize them

04/18/23 28

ConclusionConclusion

• Security and trustworthiness are BOTH very important for wireless network and its applications

• A holistic framework better secures wireless network than the existing solutions– Context makes you better understand the threats– Policy makes your countermeasure more accurate and

adaptive

04/18/23 29

Thank You Thank You •Questions?

• Email: [email protected]

04/18/23 30

Documents

Secure and Trustworthy Data Management for Vehicular Cyber Physical Systems Dr. Wenjia Li Assistant Professor in Computer Science New York Institute of