Intelligent intrusion detection systems using artificial neural networks

Alex Shenfield*, David Day, Aladdin Ayesh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

186 Citations (Scopus)

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 [1], have shown 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.

Original languageEnglish
Pages (from-to)95-99
Number of pages5
JournalICT Express
Volume4
Issue number2
Early online date1 May 2018
DOIs
Publication statusPublished - 19 Jun 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 The Korean Institute of Communications and Information Sciences (KICS)
Conflict of interest

The authors declare that there is no conflict of interest in this paper.

Keywords

  • Artificial Intelligence
  • Computer security
  • Intrusion detection systems
  • Machine learning

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