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Support vector machine for network intrusion and cyber-attack detection

Lookup NU author(s): Dr Francisco Aparicio NavarroORCiD, Professor Jonathon Chambers



This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2017.

For re-use rights please refer to the publisher's terms and conditions.


Cyber-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.

Publication metadata

Author(s): Ghanem K, Aparicio-Navarro FJ, Kyriakopoulos KG, Lambotharan S, Chambers JA

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Sensor Signal Processing for Defence (SSPD)

Year of Conference: 2017

Pages: 1-5

Online publication date: 21/12/2017

Acceptance date: 06/09/2017

Date deposited: 08/09/2017

Publisher: IEEE


DOI: 10.1109/SSPD.2017.8233268

Library holdings: Search Newcastle University Library for this item

ISBN: 9781538616635