Intrusion detection system using a new fuzzy rule-based classification system based on genetic algorithm
Zahra Asghari Varzaneh, Marjan Kuchaki Rafsanjani
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.
Topics & Concepts
Intrusion detection systemComputer scienceData miningConstant false alarm rateAnomaly-based intrusion detection systemFuzzy logicGenetic algorithmNetwork securityFuzzy ruleAlgorithmMisuse detectionALARMFalse positive rateArtificial intelligencePattern recognition (psychology)Machine learningFuzzy setEngineeringComputer securityAerospace engineeringNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesAnomaly Detection Techniques and Applications