A distributed detection and prevention scheme from malicious nodes in vehicular networks

Summary Vehicular environments still remain vulnerable to various potential attacks because of continuous interactions and information exchange between vehicles despite the deployment of authentication techniques by communication standards. Therefore, an authenticated node with a certificate could initiate an attack while complying with implemented protocols. Some mechanisms were proposed to enhance communication technologies, but none of them was able to anticipate nodes' behavior. They also mismanage oscillating vehicles, because they evict them automatically after misbehaving. In this paper, we propose a preventive mechanism, namely, Intrusion Prevention and Detection System (IPDS), able to predict vehicles behavior. This mechanism is based on a Kalman filter to predict the future vehicles' behavior and classify them into three categories (white, gray, and black) based on their expected trustworthiness. The main concerns of this work are to prevent from various attacks, namely, DoS, false alert, and Sybil, and deal with oscillating vehicles. Therefore, IPDS treats these latter in a different way where they are introduced into a gray list and used for routing purposes until they persist in their attacks. Simulations performed using ns3 simulator highlight IPDS accuracy regarding the detection rate of malicious nodes and its good impact on the network applications performances. Copyright © 2016 John Wiley & Sons, Ltd.

[1]  Iftikhar Ahmad,et al.  Classes of attacks in VANET , 2011, 2011 Saudi International Electronics, Communications and Photonics Conference (SIECPC).

[2]  Sidi-Mohammed Senouci,et al.  Efficient data dissemination in cooperative vehicular networks , 2013, Wirel. Commun. Mob. Comput..

[3]  Sidi-Mohammed Senouci,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

[4]  Panagiotis Papadimitratos,et al.  SECURING VEHICULAR COMMUNICATIONS , 2006, IEEE Wireless Communications.

[5]  Ahmad Khademzadeh,et al.  VWCA: An efficient clustering algorithm in vehicular ad hoc networks , 2011, J. Netw. Comput. Appl..

[6]  Bertrand Ducourthial,et al.  Sybil Nodes Detection Based on Received Signal Strength Variations within VANET , 2009, Int. J. Netw. Secur..

[7]  Md. Sazzad Hossain,et al.  Security Challenges And Implementation Mechanism For Vehicular Ad Hoc Network , 2013 .

[8]  Jin-Hee Cho,et al.  Trust Management for Encounter-Based Routing in Delay Tolerant Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[9]  Ralph C. Merkle,et al.  Secure communications over insecure channels , 1978, CACM.

[10]  Vijay Laxmi,et al.  A sybil attack detection approach using neighboring vehicles in VANET , 2011, SIN '11.

[11]  Xin Liu,et al.  Modeling Context Aware Dynamic Trust Using Hidden Markov Model , 2012, AAAI.

[12]  Sidi-Mohammed Senouci,et al.  LTE4V2X — Collection, dissemination and multi-hop forwarding , 2012, 2012 IEEE International Conference on Communications (ICC).

[13]  Ivan Stojmenovic,et al.  On Data-Centric Misbehavior Detection in VANETs , 2011, 2011 IEEE Vehicular Technology Conference (VTC Fall).

[14]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[15]  Wen-Chung Chang,et al.  Detecting Sybil attacks in Wireless Sensor Networks using neighboring information , 2009, Comput. Networks.

[16]  Pin-Han Ho,et al.  A Trust Modeling Framework for Message Propagation and Evaluation in VANETs , 2010, 2010 2nd International Conference on Information Technology Convergence and Services.

[17]  Pin-Han Ho,et al.  Security in vehicular ad hoc networks , 2008, IEEE Commun. Mag..

[18]  Chen Chen,et al.  Trust modeling for message relay control and local action decision making in VANETs , 2013, Secur. Commun. Networks.

[19]  Bin Xiao,et al.  Detection and localization of sybil nodes in VANETs , 2006, DIWANS '06.

[20]  Maxim Raya,et al.  Securing vehicular ad hoc networks , 2007, J. Comput. Secur..

[21]  Abdelfettah Belghith,et al.  A distributed advanced analytical trust model for VANETs , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[22]  F. Sabahi.  Impact of Threats on Vehicular Adhoc Network Security , 2012 .

[23]  Johann van der Merwe,et al.  A survey on peer-to-peer key management for mobile ad hoc networks , 2007, CSUR.

[24]  Licia Capra,et al.  Autonomic trust prediction for pervasive systems , 2006, 20th International Conference on Advanced Information Networking and Applications - Volume 1 (AINA'06).

[25]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[26]  Sidi-Mohammed Senouci,et al.  A new Intrusion Detection Framework for Vehicular Networks , 2014, 2014 IEEE International Conference on Communications (ICC).

[27]  Jianhong Zhang,et al.  Privacy-preserving authentication protocols with efficient verification in VANETs , 2014, Int. J. Commun. Syst..

[28]  Panagiotis Papadimitratos,et al.  Eviction of Misbehaving and Faulty Nodes in Vehicular Networks , 2007, IEEE Journal on Selected Areas in Communications.

[29]  Antonio Alfredo Ferreira Loureiro,et al.  Malicious node detection in wireless sensor networks , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[30]  Jizhong Zhao,et al.  Footprint: Detecting Sybil Attacks in Urban Vehicular Networks , 2012, IEEE Transactions on Parallel and Distributed Systems.

[31]  Mosa Ali Abu-Rgheff,et al.  An Efficient and Lightweight Intrusion Detection Mechanism for Service-Oriented Vehicular Networks , 2014, IEEE Internet of Things Journal.

[32]  Marco Fiore,et al.  Verification and Inference of Positions in Vehicular Networks through Anonymous Beaconing , 2014, IEEE Transactions on Mobile Computing.