Adversarial Learning for Intrusion Detection in Wireless Sensor Networks

Abstract

In the vital domain of wireless sensor networks (WSNs), which play an essential role in monitoring and data collection across diverse environments, safeguarding against cyber threats is of paramount importance. To address this challenge, this study unveils a pioneering system named “SecureNet.” This system employs the innovative technique of adversarial learning through Generative Adversarial Networks (GANs), enriched with a cutting-edge machine learning method known as deep learning, to bolster intrusion detection capabilities. Essentially, SecureNet operates by initiating a continuous competitive scenario between two deep learning models: one is designed to generate synthetic data that simulates cyber-attacks, while its counterpart focuses on identifying and distinguishing these simulated attacks from real threats. This relentless competition not only enhances SecureNet's proficiency in recognizing actual cyber-attacks.