Prevention and Localization of MAC Address Spoofing Attacks in Wireless Networks

Wireless spoofing attacks is easy to launch and can impact the performance of networks. The identity of a node can be checked through cryptographic authentication, conventional security approaches are not always desirable because of their upstairs necessities. In this paper, we offer to use spatial information, a physical property associated with each node, hard to fake, and not reliant on cryptography, as the basis for (1) detecting parody attacks; (2) determining the number of assailants when numerous adversaries masquerading as a same node identity; and (3) localizing large adversaries. We suggest to use the longitudinal relationship of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. We then prepare the problem of regulating the number of attackers as a multi-class detection problem. Cluster-based mechanisms are created to determine the number of attackers. When the training data is available, we then added Support Vector Machines method to improve the accuracy of determining the number of attackers. we developed an racially mixed detection and localization system that can plot the positions of multiple attackers. We evaluated our techniques by two test beds using both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real office buildings. Our experimental results show that our submitted methods can achieve over 90% Hit Rate and Precision when determining the number of hackers. Our localization results using a representative set of algorithms provide strong evidence of high accuracy of focusing numerous adversaries.