MAM-ISSIDS: multi-agent model-based intelligent and self-sharing intrusion detection system for distributed network. (2017)

• Record Type: Journal Article
• Authors: Anusha, K.; Sathiyamoorthy, E.
• Is Part Of: International journal of information and computer security. Volume 9:Number 4(2017)
• Page Start: 361
• Page End: 386
• Publication Date: 2017
• Subjects: intrusion detection -- intuitionistic fuzzy rules -- multi-agent model -- MAM -- particle swarm optimisation-genetic algorithm -- PSO-GA; Computer security -- Periodicals; Information systems management -- Security measures -- Periodicals; Computer networks -- Security measures -- Periodicals; Information technology -- Security measures -- Periodicals; 005.805
• Languages: English
• ISSNs: 1744-1765

Abstract

Intrusion detection system (IDS) is essential for protecting the computer networks from various threats and attacks. The autonomous multi-agent model (MAM) architecture is a scalable and smart alternative to leverage the strengths of the host and network based IDS. This paper proposes MAM-based intelligent and self-sharing IDS (MAM-ISSIDS) for distributed network to detect the host, network and web service attacks. Feature selection is performed by using the integrated particle swarm optimisation-genetic algorithm (PSO-GA) approach. The intuitionistic fuzzy rules are used to formulate the rules of the existing attackers for the benchmark dataset. The ontology structure is used to share the rules in network. The MAM is used for detecting the occurrence of abnormal traffic resulting due to the intrusion attacks. The proposed system achieves higher attack detection rate, accuracy and lower false positive rate due to the distributed sharing strategy of the MAM.