Intrusion detection system using a new fuzzy rule-based classification system based on genetic algorithm

Article type: Research Article

Authors: Varzaneh, Zahra Asghari | Kuchaki Rafsanjani, Marjan^*

Affiliations: Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran

Correspondence: [*] Corresponding author: Marjan Kuchaki Rafsanjani, Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran. E-mail: kuchaki@uk.ac.ir.

Abstract: Intrusion can compromise the integrity, confidentiality, or availability of a computer system. Intrusion Detection System (IDS) is a type of security software designed to monitor network traffic and identify network intrusions. In this paper, A Fuzzy Rule – Based classification system is used to detect intrusion in a computer network. In order to improve the classification rate, a new method is proposed based on Genetic Algorithm (GA) for rule weights specification. The proposed method is tested on KDD99 dataset. Experimental results show the proposed method improves the performance of the fuzzy rule-based classification systems in terms of detection rate and false alarm rate.

Keywords: Intrusion detection, fuzzy rule-based, rule weighting, genetic algorithm

DOI: 10.3233/IDT-200036

Journal: Intelligent Decision Technologies, vol. 15, no. 2, pp. 231-237, 2021