Detecting Access Point Spoofing Attacks Using Partitioning-based Clustering

摘要: The impersonation of wireless Access Point (AP) poses an unprecedented number threats that can compromise a client’s identity, personal data, and network integrity. AP attack is conducted by establishing rogue with spoofed Service Set Identifier (SSID) MAC address same as the target legitimate AP. Since these identities be easily forged, there no identifier used to identify Due strong correlation between signal strength distance, in this paper, we propose client-centric spoofing detection framework exploiting statistical relationship from APs. We show signals determined using two classical partitioning-based clustering methods, K-means K-medoids analysis. experimental results both analysis methods achieve over 90% rate