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Asymptotic Random Distortion Testing for Anomaly Detection

Dominique Pastor 1, 2 Guillaume Ansel 1, 2
2 Lab-STICC_MATRIX - Equipe Models and AlgoriThms for pRocessIng and eXtracting information
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
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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Submitted on : Tuesday, June 15, 2021 - 1:47:18 PM
Last modification on : Monday, October 11, 2021 - 2:23:58 PM
Long-term archiving on: : Thursday, September 16, 2021 - 6:44:01 PM

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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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