Total views : 129
Design of Misbehavior Detection Scheme by Combining Lane Change and Braking Alerts
Objective: To design and develop an enhanced Misbehaviour Detection Scheme (MDS) that addresses the problem of transmitting false information in Vehicular Ad hoc Network (VANET). Methods/Analysis: To achieve the purpose of this paper, data was collected through simulating a vehicle crash in different traffic scenarios. The data collected was then used to design a Misbehaviour Detection Scheme considering two inputs of Emergency Electronic Brake Light (EEBL) and Lane Change (LC). To confirm the veracity of transmitted Post-Crash Notification (PCN) alert, Bayes’ rule was used to combine the two alert evidences. Findings: In each of the experiments conducted, the scenario belief values (probability of individual events) were calculated and Bayes’ rule was used for combining the two evidences to obtain a better belief value. Simulation results show that increasing vehicle speed improves detection accuracy. Traffic scenarios having vehicles with low speed transmits fewer secondary alerts. Existing MDS uses single secondary alerts for verifying received PCN alerts. The proposed scheme combines evidences from more than one secondary alert to enhance the belief value of received PCN alert. Applications/Improvements: Combining multiple alert evidences shows that the proposed MDS makes significant enhancement to the existing scheme. Testing the proposed scheme with vehicles on high speeds shows 100% detection accuracy for transmitted PCN alerts.
Braking Alerts, Lane Change, Misbehaviour Detection, Post-Crash Notification, VANET.
- WHO. 10 Facts on Global Road Safety, 2014. Date Accessed: 24/3/2016. Available at: www. Goggle.com.
- Stephen E. Explaining International IT Application Leadership: Intelligent Transportation Systems. The Information Technology and Innovation Foundation, Jan 2010.
- Harit SK, Singh G, Tyagi N. Fox-Hole Model for Data-Centric Misbehavior Detection in VANETs. Paper Presented at the Computer and Communication Technology (ICCCT), Nov 2012, p. 271-7.
- Maria EM, Arun KP. Threat Analysis and Defence Mechanisms in VANET, International Journal of Advanced Research in Computer Science and Software Engineering.2013 Jan; 3(1):47-53.
- Ruj S, Cavenaghi MA, Zhen Huang, Nayak A, Stojmenovic I. On Data-Centric Misbehavior Detection in VANETs.Paper Presented at the Vehicular Technology Conference (VTC Fall), IEEE, 2011 Sep, p.1-5.
- Raya M, Papadimitratos P, Aad I, Jungels D, Hubaux JP. Eviction of Misbehaving and Faulty Nodes in Vehicular Networks, IEEE Journal on Selected Areas in Communications. 2007; 25(8):1557-68.
- Hoa LAV, Cavalli A. Security Attacks and Solutions in Vehicular Ad Hoc Networks: A Survey, International Journal on Ad Hoc Networking Systems (IJANS). 2014; 4(2).
- Fuad AG, Anazida Z, Murad A Rassam. Data Verification and Misbehaviour Detection in Vehicular Ad-hoc Networks, Journal Technology Sciences and Engineering.2015; 73(2):37–44.
- Georgios K, Onur A, Eylem E, Geert H, Boangoat J, Kenneth L, Timothy Weil. Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions, IEEE Communications Surveys and Tutorials, Fourth Quarter. 2011; 13(4):584–616.
- Uzma K, Shikha A, Sanjay S. A Detailed Survey on Misbehavior Node Detection Techniques in Vehicular Ad Hoc Networks. Information Systems Design and Intelligent Applications, Advances in Intelligent Systems and Computing, 2015, p. 339.
- Mainak G, Anitha V, Arobinda G, Arzad AK, Skanda NM.Detecting Misbehaviors in VANET with Integrated RootCause Analysis, Ad Hoc Networks. 2010; 8(7):778-90.
- Philippe G, Dan G, Jessica S. Detecting and Correcting Malicious Data in VANETs. Paper presented at the Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks, Philadelphia, PA, USA. 2004, p.29-37.
- Daeinabi A, Rahbar AG. Detection of Malicious Vehicles (DMV) through Monitoring in Vehicular Ad-Hoc Networks, Multimedia Tools Appl. 2013; 66(2):325–38.
- Kadam, M, Limkar S. New Approach for Detection and Prevention of Misbehave/Malicious Vehicles from VANET. In: Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). AISC, 2013, 247, p. 287–95.
- Vulimiri A, Gupta A, Roy P, Muthaiah SN, Kherani AA.Application of Secondary Information for Misbehavior Detection in VANETs, 9th International IFIP-TC6 Networking Conference, Networking Chennai. 2010; 6091:385-96.
- Zhen H, Sushmita R, Marcos C, Amiya N. Limitations of Trust Management Schemes in VANET and Countermeasures. IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications, 2011, p.1228-32.
- Wei YC, Chen YM. Adaptive Decision Making for Improving Trust Establishment in VANET. 16th AsiaPacific Network Operations and Management Symposium (APNOMS), 2014, p.1-4.
- Barnwal RP, Ghosh SK. Heartbeat Message Based Misbehavior Detection Scheme for Vehicular Ad-Hoc Networks. In: 2012 International Conference on Connected Vehicles and Expo (ICCVE), 2012, p. 29–34.
- Huang D, Williams SA, Shere S. Cheater Detection in Vehicular Networks. In: IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2012, p. 193–200.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.