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dc.contributor.authorGhanem, Kinan
dc.contributor.authorAparicio-Navarro, Francisco J.
dc.contributor.authorKyriakopoulos, Konstantinos
dc.contributor.authorLambotharan, Sangarapillai
dc.contributor.authorChambers, Jonathon A.
dc.date.accessioned2019-06-12T07:51:49Z
dc.date.available2019-06-12T07:51:49Z
dc.date.issued2017-12-21
dc.identifier.citationGhanem, K., Aparicio-Navarro, F.J., Kyriakopoulos, K., Lambotharan, S., Chambers, J.A. (2017) Support Vector Machine for Network Intrusion and Cyber-Attack Detection. In proceedings of 2017 Sensor Signal Processing for Defence Conference (SSPD). London, UK, December 2017.en
dc.identifier.isbn9781538616635
dc.identifier.urihttps://www.dora.dmu.ac.uk/handle/2086/18004
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractCyber-security threats are a growing concern in networked environments. The development of Intrusion Detection Systems (IDSs) is fundamental in order to provide extra level of security. We have developed an unsupervised anomaly-based IDS that uses statistical techniques to conduct the detection process. Despite providing many advantages, anomaly-based IDSs tend to generate a high number of false alarms. Machine Learning (ML) techniques have gained wide interest in tasks of intrusion detection. In this work, Support Vector Machine (SVM) is deemed as an ML technique that could complement the performance of our IDS, providing a second line of detection to reduce the number of false alarms, or as an alternative detection technique. We assess the performance of our IDS against one-class and two-class SVMs, using linear and non-linear forms. The results that we present show that linear two-class SVM generates highly accurate results, and the accuracy of the linear one-class SVM is very comparable, and it does not need training datasets associated with malicious data. Similarly, the results evidence that our IDS could benefit from the use of ML techniques to increase its accuracy when analysing datasets comprising of non-homogeneous features.en
dc.language.isoenen
dc.publisherIEEEen
dc.subjectClassification Algorithmsen
dc.subjectCyber Securityen
dc.subjectIntrusion Detection Systemsen
dc.subjectMachine Learning Techniquesen
dc.subjectNetwork Securityen
dc.subjectSupport Vector Machineen
dc.subjectSVMen
dc.titleSupport Vector Machine for Network Intrusion and Cyber-Attack Detectionen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1109/sspd.2017.8233268
dc.peerreviewedYesen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/K014307/2en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2017-09-06
dc.exception.reasonavailable on L'boro uni repositoryen
dc.exception.ref2021codes254aen
dc.funder.otherDstl/MoDen


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