Risk And Protection For Machine Learning-based Network Intrusion

This article discusses the risks associated with machine learning-based intrusion detection models, including non-explainable results and adversarial attacks. It also explores new advanced protections for these models, such as federal learning, generative adversarial networks, contrastive learning, and blockchain. These emerging techniques can establish hybrid protection solutions to prevent new risks in intrusion detection.