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Intrusion Detection based on K-Means Clustering and Ant Colony Optimization: A Survey
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 79 - Number 6 |
| Year of Publication: 2013 |
| Authors: Chetan Gupta, Amit Sinhal, Rachana Kamble |
10.5120/13747-1555
|
Chetan Gupta, Amit Sinhal, Rachana Kamble . Intrusion Detection based on K-Means Clustering and Ant Colony Optimization: A Survey. International Journal of Computer Applications. 79, 6 ( October 2013), 30-35. DOI=10.5120/13747-1555
Abstract
Identifying intrusions is the process called intrusion detection. In simple manner the act of comprising a system is called intrusion. An intrusion detection system (IDS) inspects all inbound and outbound activity and identifies suspicious patterns that may indicate a system attack from someone attempting to compromise a system. If we think of the current scenario then several new intrusion that cannot be prevented by the previous algorithm, IDS is introduced to detect possible violations of a security policy by monitoring system activities and response in all times for betterment. If we uncover the counterfeit marque in a circumspect bulletin climate, an affirmation seat is initiated to prophesy or lessen the damage to the system. As a result it is a keen intrigue. In this dissertation we survey several aspects with the traditional techniques of intrusion detection we elaborate our proposed work. We also come with some future suggestions, which can provide a better way in this direction. For the above survey we also discuss K-Means and Ant Colony optimization (ACO).
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