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Research Article Open Access
Wireless networks provide various advantages in real world. This can help businesses to increase their productivity, lower cost and effectiveness, increase scalability and improve relationship with business partners and attract customers. Communication in wireless network is critical and challenging issue. Wireless spoofing attacks are occurs easily and reduce the networks performance. Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. The flexibility and openness of wireless networks enables an adversary to masquerade as other devices easily. The traditional approach to detect spoofing attacks is to apply cryptographic authentication. Here using spatial information, a physical property of each node, so hard to falsify and not depend on cryptographic security, on the beginning for (1) detecting spoofing attacks; (2) determining the number of attackers when multiple node pretend as a same node identity, and (3) localizing multiple adversaries. Here using the correlation between a signal's spatial direction and the average received signal gain of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. Then the problem of determining the number of attackers as multiclass detection problem is formulated. Cluster-based mechanisms are developed to determine the number of attackers. When the training data is available, Support Vector Machines (SVM) method is used to further improve the accuracy of determining the number of attackers. The approach can both detects the presence of attacks as well as determine the number of adversaries, spoofing the same node identity, also that can localize any number of attackers and eliminate them.