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Vehicular ad hoc networks (VANETs) use unidentified routing protocols that hide node uniqueness and which protects the node from outside observers to provide privacy protection. However, existing unidentified routing protocols depend on either hop-by-hop encryption or unnecessary traffic whichever makes high cost or cannot provide full privacy protection to data, sender, receiver, and routes. The high cost intensifies the essential resource constraint problem in VANET. To offer high privacy protection at a low cost, we propose an unidentified location-based efficient routing protocol. Here, using digital signatures and public key infrastructure (PKI) and mix zone-based security to protect message integrity is sufficient taking into account multilateral security. Main goal is to develop security architecture for VANETs that balances security requirements of all participants and also tries to identify to develop feasible mechanisms that fit in this architecture. This balances security requirements of all participants while keeping in mind the real time.

Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. In this paper, we propose to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for detecting spoofing attacks, determining the number of attackers when multiple adversaries masquerading as asame node identity; andlocalizing multiple adversaries. We propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. We then formulate the problem of determining the number of attackers as multiclass detection problem.Cluster based mechanisms is developed to determine the number of attackers. When the training data is available, we explore using Support Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers. In addition, we developed an integrated detection and localization system that can localize the positions of multiple attackers. We evaluated our techniques through two test beds using both an 802.11 (Wi-Fi) network and an 802.15.4 (ZigBee) network in two real office buildings. Our experimental results show that our proposed methods can achieve over 90% Hit Rate and Precision when determining the number of attackers. Our localization results using a representative set of algorithms provide strongevidence of high accuracy of localizing multiple adversaries.

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