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Self-Supervised and Interpretable Anomaly Detection using Network Transformers
[article]
2022
arXiv
pre-print
Monitoring traffic in computer networks is one of the core approaches for defending critical infrastructure against cyber attacks. Machine Learning (ML) and Deep Neural Networks (DNNs) have been proposed in the past as a tool to identify anomalies in computer networks. Although detecting these anomalies provides an indication of an attack, just detecting an anomaly is not enough information for a user to understand the anomaly. The black-box nature of off-the-shelf ML models prevents extracting
arXiv:2202.12997v1
fatcat:sfc2fril45hv5lemcnoyltc7ly