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Asymptotic Random Distortion Testing for Anomaly Detection

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Abstract

In connection with cybersecurity issues in ICS, we consider the problem of detecting yet unknown attacks by presenting a theoretical framework for the detection of anomalies when the observations have unknown distributions. We illustrate the relevance of this framework with experimental results.
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Dates and versions

hal-03261082 , version 1 (15-06-2021)

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  • HAL Id : hal-03261082 , version 1

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Dominique Pastor, Guillaume Ansel. Asymptotic Random Distortion Testing for Anomaly Detection. 1st IFSA Winter Conference on Automation, Robotics & Communications for Industry 4.0 (ARCI’ 2021), Feb 2021, Chamonix, France. ⟨hal-03261082⟩
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