Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback. (arXiv:2210.02841v1 [cs.CR])

Anomaly detection is a ubiquitous and challenging task relevant across many
disciplines. With the vital role communication networks play in our daily
lives, the security of these networks is imperative for smooth functioning of
society. To this end, we propose a novel self-supervised deep learning
framework CAAD for anomaly detection in wireless communication systems.
Specifically, CAAD employs contrastive learning in an adversarial setup to
learn effective representations of normal and anomalous behavior in wireless
networks. We conduct rigorous performance comparisons …

adversarial expert feedback network