Intelligent Intrusion Detection System Through Combined and Optimized Machine Learning

Shah, Syed Ali Raza, Issac, Biju and Jacob, Seibu Mary (2018) Intelligent Intrusion Detection System Through Combined and Optimized Machine Learning. International Journal of Computational Intelligence and Applications, 17 (02). p. 1850007. ISSN 1469-0268

Full text not available from this repository.

Abstract

In this paper, an existing rule-based intrusion detection system (IDS) is made more intelligent through the application of machine learning. Snort was chosen as it is an open source software and though it was performing well, it showed false positives (FPs). To find the best performing machine learning algorithms (MLAs) to use with Snort so as to improve its detection, we tested some algorithms on three available datasets. Support vector machine (SVM) was chosen along with fuzzy logic and decision tree based on their accuracy. Combined versions of algorithms through ensemble SVM along with other variants were tried on the generated traffic of normal and malicious packets at 10Gbps. Optimized versions of the SVM along with firefly and ant colony optimization (ACO) were also tried, and the accuracy improved remarkably. Thus, the application of combined and optimized MLAs to Snort at 10Gbps worked quite well.

Item Type:	Article
Uncontrolled Keywords:	Snort intrusion detection, machine learning, support vector machine, fuzzy logic, firefly, ACO
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	21 Sep 2018 08:51
Last Modified:	11 Oct 2019 19:15
URI:	http://nrl.northumbria.ac.uk/id/eprint/35850

Downloads