A Result Evolution of An Artificial Immune System for Intrusion Detection System to Improve the Detection Rate
Pallvi Dehariya1, Shiv K Sahu2, Amit Mishra3
1Pallvi Dehariya, M.Tech. Scholar, Department of Information Technology, Technocrats Institute of Technology, Bhopal (M.P.), India.
2Dr. Shiv K Sahu, Professor & Head, Department of Information Technology, Technocrats Institute of Technology, Bhopal (M.P.), India.
3Amit Mishra, Asst. Professor, Department of Information Technology, Technocrats Institute of Technology, Bhopal (M.P.), India.
Manuscript received on 13 June 2016 | Revised Manuscript received on 20 June 2016 | Manuscript Published on 30 June 2016 | PP: 75-78 | Volume-5 Issue-5, June 2016 | Retrieval Number: E4607065516/16©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents an intrusion detection system architecture based on the artificial immune system concept. In this architecture, an innate immune mechanism through unsupervised machine learning methods is proposed to primarily categorize network traffic to “self” and “non-self” as normal and suspicious profiles respectively. Unsupervised machine learning techniques formulate the invisible structure of unlabeled data without any prior knowledge. The novelty of this work is utilization of these methods in order to provide online and realtime training for the adaptive immune system within the artificial immune system. The proposed intrusion detection system will use the concepts of the artificial immune systems (AIS) which is a promising biologically inspired computing model. AIS concepts that can be applied to improve the effectiveness of IDS.
Keywords: Intrusion Detection System, Artificial Immune System, Clustering
Scope of the Article: Clustering