An ensemble clustering method for intrusion detection
by Kapil K. Wankhade; Kalpana C. Jondhale
International Journal of Intelligent Engineering Informatics (IJIEI), Vol. 7, No. 2/3, 2019

Abstract: The amount of data in the field of computer networking is growing rapidly and this urges new challenges in the field of an intrusion detection system (IDS). To handle such increasing volume of data, a new hybrid approach has to be developed to overcome the problems such as high detection rate and low false alarm rate. An intrusion detection system plays a vital role in the detection of malicious attacks. Data mining and machine learning techniques are important and play a vital role in the detection of attacks. This paper mainly focuses on detection rate and false alarm rate and so to resolve these problems a hybrid method, ensemble clustering, has been proposed. This method tries to increase detection rate with lowering false alarm rate. The method has been tested on KDDCup'99 network intrusion dataset and performs well as compared with other algorithms in terms of detection rate and false alarm rate.

Online publication date: Mon, 15-Apr-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Engineering Informatics (IJIEI):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com