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Intrusion detection in SCADA systems using machine learning techniques

Maglaras, LA and Jiang, J (2014) Intrusion detection in SCADA systems using machine learning techniques Proceedings of 2014 Science and Information Conference, SAI 2014. pp. 626-631.

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Abstract

© 2014 The Science and Information (SAI) Organization.In this paper we present a intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition) system. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM (One-Class Support Vector Machine) is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automate SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detect anomalies in the system real time. The module is part of an IDS (Intrusion Detection System) system developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF (Intrusion Detection Message Exchange Format) messages that carry information about the source of the incident, the time and a classification of the alarm.

Item Type: Article
Authors :
NameEmailORCID
Maglaras, LAl.maglaras@surrey.ac.ukUNSPECIFIED
Jiang, Jjianmin.jiang@surrey.ac.ukUNSPECIFIED
Date : 1 January 2014
Identification Number : 10.1109/SAI.2014.6918252
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:32
Last Modified : 17 May 2017 15:11
URI: http://epubs.surrey.ac.uk/id/eprint/839575

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