D-SCIDS: distributed soft computing intrusion detection system

  • Authors:
  • Ajith Abraham;Ravi Jain;Johnson Thomas;Sang Yong Han

  • Affiliations:
  • School of Computer Science and Engineering, Chung-Ang University, Korea;University of South Australia, Adelaide, Australia;Computer Science Department, Oklahoma State University, OK;School of Computer Science and Engineering, Chung-Ang University, Korea

  • Venue:
  • Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. A Distributed IDS (DIDS) consists of several IDS over a large network (s), all of which communicate with each other, or with a central server that facilitates advanced network monitoring. In a distributed environment, DIDS are implemented using co-operative intelligent agents distributed across the network(s). This paper evaluates three fuzzy rule-based classifiers to detect intrusions in a network. Results are then compared with other machine learning techniques like decision trees, support vector machines and linear genetic programming. Further, we modeled Distributed Soft Computing-based IDS (D-SCIDS) as a combination of different classifiers to model lightweight and more accurate (heavy weight) IDS. Empirical results clearly show that soft computing approach could play a major role for intrusion detection.