Intelligent intrusion detection systems using artificial neural networks

SHENFIELD, Alex, DAY, David and AYESH, Aladdin (2018). Intelligent intrusion detection systems using artificial neural networks. ICT Express.

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Abstract

This paper presents a novel approach to detection of malicious network traffic using artificial neural networks suitable for use in deep packet inspection based intrusion detection systems. Experimental results using a range of typical benign network traffic data (images, dynamic link library files, and a selection of other miscellaneous files such as logs, music files, and word processing documents) and malicious shell code files sourced from the online exploit and vulnerability repository exploitdb \cite{exploitdb}, have show that the proposed artificial neural network architecture is able to distinguish between benign and malicious network traffic accurately. The proposed artificial neural network architecture obtains an average accuracy of 98\%, an average area under the receiver operator characteristic curve of 0.98, and an average false positive rate of less than 2% in repeated 10-fold cross-validation. This shows that the proposed classification technique is robust, accurate, and precise. The novel approach to malicious network traffic detection proposed in this paper has the potential to significantly enhance the utility of intrusion detection systems applied to both conventional network traffic analysis and network traffic analysis for cyber-physical systems such as smart-grids.

Item Type:	Article
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments - Does NOT include content added after October 2018:	Faculty of Science, Technology and Arts > Department of Engineering and Mathematics
Identification Number:	https://doi.org/10.1016/j.icte.2018.04.003
Depositing User:	Alex Shenfield
Date Deposited:	16 Apr 2018 11:40
Last Modified:	17 Mar 2021 20:46
URI:	https://shura.shu.ac.uk/id/eprint/20882

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