Wavelet Based Detection of Session Hijacking Attacks in Wireless Networks

This paper develops a mechanism for detecting session hijacking attacks in wireless networks. The proposed scheme is based on detecting abrupt changes in the strength of the received signal. We first develop a mathematical model to describe the signal strength during a session hijacking: a step function signal, which represents the abrupt jump in the signal strength, imbedded in colored noise, which is caused by fading wireless channels. An optimal filter is designed for the purpose of detection. We show that using a wavelet transform (WT), the colored noise with complex power spectral density (PSD) in our case can be approximately whitened. Since larger signal to noise ratio (SNR) increases the detection rate and decreases the false alarm rate, we maximize the SNR by analyzing the signal at specific ranges of frequency. We validate the detection mechanism by simulation and experimental results.