Zero Day Threat Detection Using Metric Learning Autoencoders. (arXiv:2211.00441v1 [cs.CR])

The proliferation of zero-day threats (ZDTs) to companies' networks has been
immensely costly and requires novel methods to scan traffic for malicious
behavior at massive scale. The diverse nature of normal behavior along with the
huge landscape of attack types makes deep learning methods an attractive option
for their ability to capture highly-nonlinear behavior patterns. In this paper,
the authors demonstrate an improvement upon a previously introduced
methodology, which used a dual-autoencoder approach to identify ZDTs in network
flow telemetry. …

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