By Topic

A comparison of data mining techniques for intrusion detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Naidu, R.C.A. ; Dept. of CS & SE, Andhra Univ., Visakhapatnam, India ; Avadhani, P.S.

The Expositional increase in the traffic across networks has necessitated the need to detect unauthorized access. In this sense Intrusion Detection has become one of the major research areas In this paper three data mining techniques namely C5.0 Decision Tree, Ripper Rule and Support Vector Machines are studied and compared for the efficiency in detecting the Intrusion, It is found that the C5.0 Decision Tree is efficient than the other two. The data mining tool clementine is used for evaluating this on the KDD99 dataset. The results are also given in this paper.

Published in:

Advanced Communication Control and Computing Technologies (ICACCCT), 2012 IEEE International Conference on

Date of Conference:

23-25 Aug. 2012