An Artificial Immune System and Neural Network to Improve the Detection Rate in Intrusion Detection System

Pallvi Dehariya¹

¹ Department of Information Technology, TIT (RGPV), Bhopal, India.

Correspondence should be addressed to: pallavideh90@gmail.com.

Section:Research Paper, Product Type: Journal
Vol.4 , Issue.1 , pp.1-4, Feb-2016

Online published on Feb 28, 2016

Copyright © Pallvi Dehariya . 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.
 

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Abstract :
The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organized and distributed manner. The threats and intrusions in IT systems can basically be compared to human diseases with the difference that the human body has an effective way to deal with them, what still need to be designed for IT systems. 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.

Key-Words / Index Term :
Intrusion Detection System, Artificial Immune System, Clustering

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