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|Title: ||Support vector machine for network intrusion and cyber-attack detection|
|Authors: ||Ghanem, Kinan|
Aparicio-Navarro, Francisco J.
Kyriakopoulos, Konstantinos G.
|Keywords: ||Classification algorithms|
Intrusion detection systems
Machine learning techniques
Support vector machine
|Issue Date: ||2017|
|Citation: ||GHANEM, K. ...et al., 2017. Support vector machine for network intrusion and cyber-attack detection. 2017 Sensor Signal Processing for Defence Conference (SSPD2107), London, 6-7 December 2017.|
|Abstract: ||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.|
|Description: ||© IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Sponsor: ||This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/2 and the MOD University Defence Research Collaboration in Signal Processing.|
|Version: ||Accepted for publication|
|Publisher Link: ||https://doi.org/10.1109/SSPD.2017.8233268|
|Appears in Collections:||Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)|
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