Optimal active eavesdropping using PSO

Eavesdropping is the act of secretly listening to the private conversation of others without their consent. In this paper explains how we can identify an active eavesdropper in the communication channel and how to drop it. We derive a new security attack from the pilot contamination phenomenon using PSO. Among the variety of threats and risks that wireless LANs are facing, session hijacking attacks are common and serious ones. Current techniques for detecting session hijacking attacks are mainly based on spoofable and predictable parameters such as sequence numbers, which can be guessed by the attackers. To enhance the reliability of intrusion detection systems, mechanisms that utilize the unspoofable PHY layer characteristics are needed. We derive a new security attack from the pilot contamination phenomenon, which targets at systems using reverse training to obtain the CSI at the transmitter for precoder design. A PSO based optimal filter is then designed for the purpose of detection. It's the fastest and high accuracy optimization technique. 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 results.