An Adaptive Federated Learning Intrusion Detection System Based on Generative Adversarial Networks under the Internet of Things

With the rapid advancement of artificial intelligence, the Internet of Things (IoT), and related technologies, cybersecurity concerns are escalating. Reconstruction attacks pose a substantial threat to the privacy and security of machine learning models. This paper explores innovative approaches, specifically federated learning (FL) and generative adversarial networks (GAN), to bolster intrusion detection capabilities. Federated learning, a decentralized machine learning paradigm, empowers individual network clients to conduct model training locally, mitigating privacy risks associated with centralized data uploads. While FL addresses data privacy concerns, it falls short in protecting against reconstruction attacks—malicious attempts to reconstruct sensitive data from training models.To tackle this vulnerability, we integrate GAN into the FL framework, presenting a novel method for IoT anomalous traffic intrusion detection. By combining FL's decentralized training with GAN's generative capabilities, our approach achieves heightened accuracy in identifying and thwarting reconstruction attacks. Experimental evaluations, utilizing publicly available datasets, showcase the superior performance of our method compared to traditional intrusion detection techniques.Our method not only enhances data privacy but also introduces a paradigm shift in network security research and applications. This paper contributes valuable insights and methods, opening new avenues for advancing the field of intrusion detection in the era of evolving cyber threats.