Experiences in passively detecting session hijacking attacks in IEEE 802.11 networks
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This paper proposes a mechanism for detecting session hijacking attacks in wireless networks. The proposed scheme is based on using a wavelet based analysis of the received signal strength. We first develop a model to describe the changes in the received signal strength of a wireless station during a session hijack, while the received signal is embedded in colored noise caused by fading wireless channels. An optimal filter is then 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 a larger Signal to Noise Ratio (SNR) increases the detection rate and decreases the false alarm rate, the SNR is maximized by analyzing the signal at specific frequency ranges. The detection mechanism is validated using both simulation and experimental results. The detector is shown to be reliable, computationally inexpensive and have minimal impact on the network performance.