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dc.contributor.authorChawla, Ashima
dc.contributor.authorJacob, Paul
dc.contributor.authorLee, Brian
dc.contributor.authorFallon, Sheila
dc.date.accessioned2021-10-30T11:11:17Z
dc.date.available2021-10-30T11:11:17Z
dc.date.copyright2019
dc.date.issued2019
dc.identifier.citationChawla, A., Jacob, P., Lee, B., Fallon, S. (2019) Deep neural networks for sequence based anomaly detection in cyber security. Presented at AIT Poster Presentation Seminar 2019.en_US
dc.identifier.urihttp://research.thea.ie/handle/20.500.12065/3756
dc.description.abstractCyber security has become one of the most challenging aspects of modern world digital technology and it has become imperative to minimize and possibly avoid the impact of cybercrimes. Host based intrusion detection systems help to protect systems from various kinds of malicious cyber attacks. One approach is to determine normal behaviour of a system based on sequences of system calls made by processes in the system. The proposed model describes a computationally efficient anomaly based intrusion detection system based on Recurrent Neural Networks. Using Gated Recurrent Units rather than the normal LSTM networks it is possible to obtain a set of comparable results with reduced training times. The incorporation of stacked CNNs with GRUs leads to improved anomaly IDS. Intrusion Detection is based on determining the probability of a particular call sequence occurring from a language model trained on normal call sequences from the ADFA Data set of system call traces. Sequences with a low probability of occurring are classified as an anomalyen_US
dc.formatPDFen_US
dc.publisherAthlone Institute of Technologyen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectNeural networksen_US
dc.subjectAnomaly detectionen_US
dc.subjectCyber securityen_US
dc.titleDeep neural networks for sequence based anomaly detection in cyber securityen_US
dc.typeinfo:eu-repo/semantics/otheren_US
dc.contributor.affiliationAthlone Institute of Technologyen_US
dc.contributor.sponsorEuropean Union’s Horizon 2020 Research and Innovation Programmeen_US
dc.identifier.orcidhttps://orcid.org/ 0000-0001-5933-3107en_US
dc.identifier.orcidhttps://orcid.org/ 0000-0001-5090-2756en_US
dc.identifier.orcidhttps://orcid.org/ 0000-0002-8475-4074en_US
dc.identifier.orcidhttps://orcid.org/ 0000-0001-6874-5699en_US
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessen_US
dc.subject.departmentFaculty of Engineering & Informatics AITen_US
dc.type.versioninfo:eu-repo/semantics/publishedVersionen_US
dc.relation.projectid70071en_US


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International