A spoofing attack in a wireless network context occurs when a person unauthorized to access the network pretends to be someone else by falsifying data. The spoofer thus receives illegitimate access to the network, and can inflict significant damage to network operations.
The current project presents a method to protect wireless networks from spoofing attacks by using received signal strength (RSS) measurements, which are difficult to falsify. The method identifies attackers by using the RSS measurements and spatial information to determine which nodes have a different location than legitimate wireless users. Using cluster analysis and a learning algorithm based on “support vector machines” the proposed method can: detect the occurrence of a spoofing attack; determine the number of attackers when there are multiple spoofers using the same identity; localize the attackers and eliminate them from a network. Simulation results show that the RSS-based spatial correlation method is highly effective in detecting and determining the number of attackers in the network.
|Commitee:||Ary, James, Tsang, Chit-Sang|
|School:||California State University, Long Beach|
|School Location:||United States -- California|
|Source:||MAI 55/03M(E), Masters Abstracts International|
|Subjects:||Information Technology, Electrical engineering|
|Keywords:||Received signal strength, Signal strength, Spoofing attackers, Wireless network|
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