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- W2625243865 abstract "As Industrial Control Systems (ICSs) become more and more connected it followsthat they need to become more secure. Traditional Intrusion Detection Systems(IDSs) do not work well due to the fact that they mostly work on a signaturebasis and there are not many known signatures to detect attacks on ICSs. Sincethe network traffic from an ICS is claimed to be static and signatures are scarce,searching for anomalies in the network to detect threats is more effective. Thiscan be achieved using machine learning and other statistical models, teaching thesystem to tell regular traffic from irregularities. In this thesis we survey papersfrom related work and evaluate their results, conduct a risk analysis of ICSs frompublished sources and a workshop with an industry expert. Based on the surveyand the risk analysis we analyze over 100 days of network traffic from a waterdistribution system in order to get further understanding of how ICSs act, bothconsidering network traffic and process semantics. From this work we propose andevaluate three methods to be used when creating a more data driven IDS, capableof detecting process semantic tampering within an ICS.Our results from conducted experiments exhibit a static nature of the data originatingfrom the ICS and the result from evaluating two of the three proposed methodsusing proof of concept systems, we deem that these anomaly-based detection methodswork well for both semantic tampering as well as on a network basis. Havingan IDS using a fusion of all three proposed methods, would benefit the security ofan ICS since both semantics and network behavior are taken into account." @default.
- W2625243865 created "2017-06-23" @default.
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- W2625243865 date "2017-01-01" @default.
- W2625243865 modified "2023-09-23" @default.
- W2625243865 title "Network-based Intrusion Detection Systems for Industrial Control Systems" @default.
- W2625243865 hasPublicationYear "2017" @default.
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