The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey. (arXiv:1911.02621v3 [cs.CR] UPDATED)

Machine learning models have made many decision support systems to be faster,
more accurate, and more efficient. However, applications of machine learning in
network security face a more disproportionate threat of active adversarial
attacks compared to other domains. This is because machine learning
applications in network security such as malware detection, intrusion
detection, and spam filtering are by themselves adversarial in nature. In what
could be considered an arm's race between attackers and defenders, adversaries
constantly probe machine learning systems …

adversarial adversarial attacks applications attacks decision detection domains intrusion intrusion detection machine machine learning machine learning models malware malware detection nature network network security security spam spam filtering support survey systems threat