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Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series
[article]
2019
arXiv
pre-print
Today's Cyber-Physical Systems (CPSs) are large, complex, and affixed with networked sensors and actuators that are targets for cyber-attacks. Conventional detection techniques are unable to deal with the increasingly dynamic and complex nature of the CPSs. On the other hand, the networked sensors and actuators generate large amounts of data streams that can be continuously monitored for intrusion events. Unsupervised machine learning techniques can be used to model the system behaviour and
arXiv:1809.04758v3
fatcat:lj24chtitba2lfyhxjnxbu556u