An Efficient and Lightweight Intrusion Detection Mechanism for Service-Oriented Vehicular Networks

Vehicular ad hoc networks (VANETs) are wireless networks that provide high-rate data communication among moving vehicles and between the vehicles and the road-side units. VANETs are considered as the main wireless communication platforms for the intelligent transportation systems (ITS). Service-oriented vehicular networks are special categories for VANETs that support diverse infrastructure-based commercial infotainment services including, for instance, Internet access, real-time traffic monitoring and management, video streaming. Security is a fundamental issue for these service networks due to the relevant business information handled in these networks. In this paper, we design and implement an efficient and light-weight intrusion detection mechanism, called efficient and light-weight intrusion detection mechanism for vehicular network (ELIDV) that aims to protect the network against three kinds of attacks: denial of service (DoS), integrity target, and false alert's generation. ELIDV is based on a set of rules that detects malicious vehicles promptly and with high accuracy. We present the performance analysis of our detection mechanism using NS-3 simulator. Our simulation results show that ELIDV exhibits a high-level security in terms of highly accurate detection rate (detection rate more than 97%), low false positive rate (close to 1%), and exhibits a lower overhead compared to contemporary frameworks.

[1]  Sidi-Mohammed Senouci,et al.  Efficient and lightweight intrusion detection based on nodes' behaviors in wireless sensor networks , 2013, Global Information Infrastructure Symposium - GIIS 2013.

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

[3]  Tao Zhang,et al.  Defending Connected Vehicles Against Malware: Challenges and a Solution Framework , 2014, IEEE Internet of Things Journal.

[4]  David A. Wagner,et al.  Secure verification of location claims , 2003, WiSe '03.

[5]  A. Perrig,et al.  The Sybil attack in sensor networks: analysis & defenses , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[6]  Naveen K. Chilamkurti,et al.  Collaborative trust aware intelligent intrusion detection in VANETs , 2014, Comput. Electr. Eng..

[7]  Yang Yang,et al.  Analysis of collision probability in vehicular ad hoc networks , 2009, GEC '09.

[8]  Javier Gozálvez,et al.  Contention-based forwarding with multi-hop connectivity awareness in vehicular ad-hoc networks , 2013, Comput. Networks.

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

[10]  Athanasios V. Vasilakos,et al.  DTRAB: Combating Against Attacks on Encrypted Protocols Through Traffic-Feature Analysis , 2010, IEEE/ACM Transactions on Networking.

[11]  Ivan Stojmenovic,et al.  Data-centric Misbehavior Detection in VANETs , 2011, ArXiv.

[12]  Yasir Saleem,et al.  Network Simulator NS-2 , 2015 .

[13]  Pin-Han Ho,et al.  An Efficient Identity-Based Batch Verification Scheme for Vehicular Sensor Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[14]  Issa M. Khalil,et al.  LITEWORP: a lightweight countermeasure for the wormhole attack in multihop wireless networks , 2005, 2005 International Conference on Dependable Systems and Networks (DSN'05).

[15]  Simone A. Ludwig,et al.  Evaluating Workflow Trust Using Hidden Markov Modeling and Provenance Data , 2013 .

[16]  Haojin Zhu,et al.  Security in service-oriented vehicular networks , 2009, IEEE Wireless Communications.

[17]  Hassan Artail,et al.  A Framework for Secure and Efficient Data Acquisition in Vehicular Ad Hoc Networks , 2013, IEEE Transactions on Vehicular Technology.

[18]  Sidi-Mohammed Senouci,et al.  An efficient intrusion detection framework in cluster-based wireless sensor networks , 2013, Secur. Commun. Networks.

[19]  Bo Yu,et al.  Detecting Sybil attacks in VANETs , 2013, J. Parallel Distributed Comput..

[20]  S. Cherkaoui,et al.  Service Discovery and Service Access in Wireless Vehicular Networks , 2008, 2008 IEEE Globecom Workshops.

[21]  Jessica Staddon,et al.  Detecting and correcting malicious data in VANETs , 2004, VANET '04.