An Efficient Neural Network Algorithm for Physical Layer Spoofing Attack Detection

Abstract

Spoofing attacks, which impersonate legitimate users, pose significant challenges to communication security by exploiting the dependence of received signal strength (RSS) on the spatial position of the transmitter. An enhanced GA_BPNNC algorithm was proposed to learn the distribution of RSS vectors to classify positions, distinguishing between attackers and legitimate users. The algorithm's performance was evaluated using real datasets which are collected in a room of the University of California, San Diego, demonstrating accuracy and robustness compared to existing neural network models. Our method achieved accuracy of over 95% and execution time of less 0.56 s. The experimental results indicate that the proposed algorithm outperforms other state-of-the-art algorithms, with the advantage of not relying on specific communication protocols, offering high throughput and fast decision-making capabilities.