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Hybrid deep neural network anomaly detection system for SCADA networks

Raogo Kabore 1, 2
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : SCADA systems are more and more targeted by cyber-attacks because of many vulnerabilities inhardware, software, protocols and the communication stack. Those systems nowadays use standard hardware, software, operating systems and protocols. Furthermore, SCADA systems which used to be air-gaped are now interconnected to corporate networks and to the Internet, widening the attack surface.In this thesis, we are using a deep learning approach to propose an efficient hybrid deep neural network for anomaly detection in SCADA systems. The salient features of SCADA data are automatically and unsupervisingly learnt, and then fed to a supervised classifier in order to dertermine if those data are normal or abnormal, i.e if there is a cyber-attack or not. Afterwards, as a response to the challenge caused by high training time of deep learning models, we proposed a distributed approach of our anomaly detection system in order lo lessen the training time of our model.
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Submitted on : Saturday, July 3, 2021 - 1:01:28 AM
Last modification on : Monday, April 4, 2022 - 9:28:20 AM
Long-term archiving on: : Monday, October 4, 2021 - 6:01:16 PM


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  • HAL Id : tel-03277329, version 1


Raogo Kabore. Hybrid deep neural network anomaly detection system for SCADA networks. Cryptography and Security [cs.CR]. Ecole nationale supérieure Mines-Télécom Atlantique, 2020. English. ⟨NNT : 2020IMTA0190⟩. ⟨tel-03277329⟩



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