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Anomaly Detection using a Clustering Technique

Anusha Jayasimhan, Jayant Gadge Published in Pattern Recognition

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
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  1. Anusha Jayasimhan and Jayant Gadge. Article: Anomaly Detection using a Clustering Technique. International Journal of Applied Information Systems 2(8):5-9, June 2012. BibTeX

    	author = "Anusha Jayasimhan and Jayant Gadge",
    	title = "Article: Anomaly Detection using a Clustering Technique",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 2,
    	number = 8,
    	pages = "5-9",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"


Computer networks are usually vulnerable to attacks by any unauthorized person trying to misuse the resources. Hence they need to be protected against such attacks by Intrusion Detection Systems (IDS). The traditional prevention techniques such as user authentication, data encryption, avoidance of programming errors, and firewalls are only used as the first line of defense. But, if a password is weak and is compromised, user authentication cannot prevent unauthorized use. Similarly, firewalls are vulnerable to errors in configuration and sometimes have ambiguous/undefined security policies. They fail to protect against malicious mobile code, insider attacks and unsecured modems. Therefore, intrusion detection is required as an additional wall for protecting systems.

Previously many techniques have been used for the effective detection of intrusions. One of the major issues is however the accuracy of these systems i. e an increase in the number of false negatives. Due to the increasing amount of new and novel types of attacks, any activity which is harmful or malicious may not be identified. To overcome this issue, a clustering technique i. e Simple K Means is used to identify and detect novel attacks and also to reduce the false negative rate.


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Anomaly Detection, Simple K Means, Feature Selection