Malicious node detection in vanet

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DETECTION OF MALICIOUS NODES IN VEHICULAR AD-

HOC NETWORK

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• PRESENTED BY: VYSAKH M

• GUIDED BY:

• CO-ORDINATED BY:

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Presentation outline• Why VANET ?• What is a VANET ?• Malicious nodes• Related work• Network model and definitions• Proposed DMN algorithm• Performance evaluation• Conclusion • Future work• References

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Why VANET? You are on the way in a car and ….

oWish to know about traffic jam condition at next turn or road condition

ahead

o Wish to have prior alert, if vehicle in front of you is applying breaks

oWish to have advance info, if any vehicles met with an accidents on the road

ahead

oWish to know whether the nearby parking area has vacancy or not

oWish to avoid accidents in ever increasing traffic conditions

A modern vehicle

F o r w a r d r a d a r

C o m p u t in g p l a t f o r m

E v e n t d a t a r e c o r d e r ( E D R )P o s i t i o n i n g s y s t e m

R e a r r a d a r

C o m m u n i c a t i o n f a c i l i t y

D i s p l a y

(GPS)

Human-Machine Interface

A modern vehicle is a network of sensors/actuators on wheels !

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What is VANET? Network of moving and smart vehicles VANET is a technology that uses moving cars as nodes to

create a mobile network Vehicles Transformed into “Computers on the Wheels” or

“Networks on the Wheel” Vehicular Communication System (VCS):- Two main type

of communications Vehicle to Vehicle (V2V) Communication:-

Vehicle to Infrastructure (V2I) communication:- Wireless communication using short range radio

technologies

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Vehicle to vehicle communication

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Vehicle to infrastructure communication

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Network Model &Definitions

VehiclesRoad Side Units(RSUs) Certificate Authorities(CAs): Authentication , Security , Management of identities , Verifying the misbehavior report and Modifying the distrust value of nodes.

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Malicious Nodes

If malicious nodes are present in a VANET, they may attempt to reduce network connectivity by pretending to be cooperative but in effect dropping any data they are mean to pass on.

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Algorithm DescriptionDMN Algorithm is based on the following three basic concepts

• A vehicle is considered to show an abnormal behavior if it drops or duplicate the packets received to it so as to create congestion in the network, misguide other vehicular nodes or destroy crucial messages for their selfish motives.

• An honest vehicle forwards the messages received to it correctly to other nodes in the network or creates right messages for transmission.

• A vehicle will be tagged as a malicious vehicle, if the vehicle repeats abnormal behavior such that its distrust value, DV exceeds the threshold value TMD.

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DMN AlgorithmNotations:VN: relaying nodeVU: verifierPL: packet latencya: no of packets received by VNb: no of packets that VN drops/duplicatesDV: distrust value

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Cont.DP: decision parameterTR: transmission rateSMX: maximum speed of vehicleSMN: minimum speed of vehicleW: weight factorTVS: selection thresholdCH: reliable node in a cluster

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Parameters for the selection of verifier

Load(LD): It refers to the no of nodes a vehicle is already

monitoring. A node which has less LD value is selected as a verifier

Distrust value (DV): It refers to the measure of trustworthiness of a

vehicle Distance(DS): Distance of vehicle from node

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Assumptions• DP=W1*LD+W2*DV+W3*DS

• W1+W2+W3=1

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Proposed DMN algorithm• Step 1:Start• Step 2: Vehicle VN joins the vehicular network• Step 3: Get the cluster keys.• Step 4: Compute the parameters- Load, Distrust Value and

Distance for the nodes in area of VN for verifier selection.• Step5: Calculate the Decision parameter for verifier

selection, DP. DP = W1 * LD + W2 * DV + W3 * DS

Where, W1 + W2 + W3 = 1. W1, W2, and W3 are the weight factors for parameters

Load (LD), Distrust Value (DV) and Distance (DS) respectively.

• Step 6: Find out nodes with Decision parameter value less then Selection Threshold,ie (DP < TVS)

• Step 7: Allocate nodes obtained from Step 6 as verifiers to the recently joined vehicle VN.

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• Step 8: Verifiers monitor behavior of vehicle VN.• Step 9: If (verifier detects vehicle VN showing abnormal

behavior) then Report to the cluster head (CH) go to step 10 else go to step 8;• Step 10: CH calculates new distrust value (DV) of VN.• Step 11: If distrust value is less than or equal to detection

threshold ie if (DV < = TMD ) then update the white list and goto 8 else go to 12• Step 12: Warning message is send to all other nodes.• Step 13: Update the entry of Vehicle VN in black list.• Step 14: Isolate the detected malicious vehicle from the

network.• Step 15:End

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Performance Evaluation Performance of the proposed DMN algorithm is

computed in terms of

Average Throughput: The amount of data transferred per unit time or

average rate of successful message transmissions. measured in bits per seconds(bits/sec)

Packet Delivery Ratio: Ratio of data packets received by destination

nodes to those produced by source node.

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Average End to End Delay: Time between origination of packet at the source

and packet delivery time at destination.

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Average Throughput

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Packet Delivery Ratio

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Average End to End Delay

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Conclusion

DMN algorithm enhance the network the network performance

Optimizes the selection of verifier nodesProvides higher throughput ,better packet delivery

ratio and reduce the end to end delay

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Future work

In order to enhance the performance of DMN algorithm we will consider other optimization techniques like PSO to select verifiers in algorithm

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References• 1. Al-kahtani, MS. Survey on security attacks in Vehicular Ad hoc

Networks (VANETs). In: 6th International Conference on Signal• Processing and Communication Systems (ICSPCS); 2012. p. 1-9.• 2. Vulimiri A, Gupta A, Roy P, Muthaiah SN, Kherani AA. Application of

Secondary Information for Misbehavior Detection in VANETs.• Springer, IFIP, LNCS 2010. 6091: 385-396.• 3. Daeinabi A, Rahbar AG. Detection of malicious vehicles (DMV)

through monitoring in Vehicular Ad-Hoc Networks. Springer, Multimedia

• Tools and Applications 2013. 66: 325-338.• 4. Barnwal RP, Ghosh SK. Heartbeat Message Based Misbehavior

Detection Scheme for Vehicular Ad-hoc Networks. In: International• Conference on Connected Vehicles and Expo (ICCVE) 2012; p. 29-34.• 5. Mishra B, Nayak P, Behera S, Jena D. Security in vehicular adhoc

networks: a survey. ACM, ICCCS; 2011. p. 590-595.

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Thank you!