On Secure and Privacy-Aware Sybil Attack Detection in Vehicular Communications (original) (raw)
2014
The foreseen dream of Vehicular Ad Hoc NETwork (VANET) deployment is obstructed by long-chased security and privacy nightmares. Despite of the increasing demand for perfect privacy, it conflicts with rather more serious security threat called ‘Sybil Attack’ which refers to, impersonation of one physical entity for many, namely Sybil nodes. In such circumstances, data received from malicious Sybil attacker may seem as if it was received from many distinct physical nodes. Sybil nodes may deliberately mislead other neighbors, resulting in catastrophic situations like traffic jams or even deadly accidents. Preventing such attacks in a privacy-enabled environment is not a trivial task. In this paper, we aim at two conflicting goals, i.e. privacy and Sybil attack in VANET. We leverage pseudonymless beaconing in order to preserve privacy. To cope with Sybil attack, we put forth a twofold strategy. In order to avoid Sybil attack through scheduled beacons, we employ tamper resistant module (TRM) to carry out a pre-assembly data analysis on data that is used to assemble beacons whereas for event reporting message (ERM), we employ road side units (RSUs) to localize Sybil nodes in VANET and report them to the revocation authority(s). RSUs distribute authorized tokens among the benign vehicular nodes which in turn are consumed to report ERMs. RSUs collect ERMs for certain event and figures out if more than one ERM for the same event includes identical token or, if an ERM is sent more than once by the same source. Our proposed scheme preserves privacy in both beacons and ERMs, and provides conditional anonymity where in case of a dispute; malicious attackers are subject to revocation. We also show that our proposed scheme outperforms the previously proposed scheme from security and computational complexity standpoint.
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The past decade has witnessed a growing interest in VANET (Vehicular Ad Hoc NETwork) and its myriad potential applications. Nevertheless, despite the surge in VANET research, security and privacy issues have been the root cause of impeded momentum in VANET deployment. In this paper we focus on misbehavior and Sybil attacks from VANET standpoint. With intrusion capabilities in hand, malicious users in VANET can inject false information and launch Sybil attack. Sybil attack refers to pretending one physical node to be many and in worst case almost all kinds of attacks can be launched in the presence of Sybil attack. Misbehavior in VANET can be categorized as a sub-effect of Sybil attack where a malicious vehicular node(s) spoof legitimate identities. There are two main strategies for avoiding misbehavior in VANET; Entity-centric strategies that focus on the revocation of misbehaving nodes by revocation authorities. On the other hand, Data-centric approach mainly focuses on the soundness of information rather than the source of information. We cover both strategies where decision on which strategy to be used, is taken on the basis of traffic situation. In a dense traffic regime, we propose SADS (Sybil Attack Detection Scheme) whereas in sparse traffic regime, we propose LMDS (Location-Based Misbehavior Detection Scheme). Our proposed schemes leverage position verification of the immediate source of warning message. Furthermore, we guarantee security and privacy (conditional anonymity) for both beacons and warning messages.
Enhanced Conditional Privacy Preservation In VANETs
The Vehicle drivers (users) do not want their personal information such as vehicle names, license plate, speed, positions, moving routes, and user information to be revealed, in order to protect them against any illegal tracing or user profiling. Thus, this information must be protected from any kind of misuse or attacks. For this the obscurity of vehicular nodes should be supported to preserve privacy of vehicles and their users. Also, we should be able to investigate for accidents or liabilities from non-repudiation. Hence, we present an enhanced conditional privacy preservation scheme for vehicular ad-hoc networks (VANETs). This scheme includes an ID-based cryptosystem to assure user's obscurity using pseudonyms; however the model provides a backdoor for authorities to track misbehaving and suspicious users.
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