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Security and Communication Networks
Volume 2017 (2017), Article ID 6216078, 15 pages
Research Article

A Fusion of Multiagent Functionalities for Effective Intrusion Detection System

1Department of ECE, Kalasalingam University, Krishnankoil, Tamil Nadu 626126, India
2Department of Instrumentation & Control Engineering, Kalasalingam University, Krishnankoil, Tamil Nadu, India

Correspondence should be addressed to Dhanalakshmi Krishnan Sadhasivan; moc.liamg@3iaj.imhskalanahd

Received 30 June 2016; Revised 17 September 2016; Accepted 10 October 2016; Published 11 January 2017

Academic Editor: Zheng Yan

Copyright © 2017 Dhanalakshmi Krishnan Sadhasivan and Kannapiran Balasubramanian. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Provision of high security is one of the active research areas in the network applications. The failure in the centralized system based on the attacks provides less protection. Besides, the lack of update of new attacks arrival leads to the minimum accuracy of detection. The major focus of this paper is to improve the detection performance through the adaptive update of attacking information to the database. We propose an Adaptive Rule-Based Multiagent Intrusion Detection System (ARMA-IDS) to detect the anomalies in the real-time datasets such as KDD and SCADA. Besides, the feedback loop provides the necessary update of attacks in the database that leads to the improvement in the detection accuracy. The combination of the rules and responsibilities for multiagents effectively detects the anomaly behavior, misuse of response, or relay reports of gas/water pipeline data in KDD and SCADA, respectively. The comparative analysis of the proposed ARMA-IDS with the various existing path mining methods, namely, random forest, JRip, a combination of AdaBoost/JRip, and common path mining on the SCADA dataset conveys that the effectiveness of the proposed ARMA-IDS in the real-time fault monitoring. Moreover, the proposed ARMA-IDS offers the higher detection rate in the SCADA and KDD cup 1999 datasets.