Online EM Monitoring of 802.11n Networks using Self Adaptive Kernel Machine

In this work, we evaluated the performances of an adaptive and online clustering algorithm (Self-Adaptive Kernel Machine - SAKM) adjusted for the automatic and online recognition of attacks on wi-fi communication (802.11n protocol). The results presented here are part of a wider project dealing with wi-fi system monitoring. The radio waves are easy to listen. Due to the quick evolution in the available attacks, the use of learning algorithm cannot cover all configurations. Online clustering constructs evolving models without knowledge of the different cases to discriminate and is therefore well suited to this type of problematic. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account variations over time.