Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems. (arXiv:2207.03576v1 [cs.CR])

Recently, advances in deep learning have been observed in various fields,
including computer vision, natural language processing, and cybersecurity.
Machine learning (ML) has demonstrated its ability as a potential tool for
anomaly detection-based intrusion detection systems to build secure computer
networks. Increasingly, ML approaches are widely adopted than heuristic
approaches for cybersecurity because they learn directly from data. Data is
critical for the development of ML systems, and becomes potential targets for
attackers. Basically, data poisoning or contamination is one …

algorithms detection intrusion intrusion detection robustness systems unsupervised learning