Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Comparative Study of Supervised Machine Learning 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
$31 $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)
Gharibian, F. ; Network Security Lab., Univ. of New Brunswick, Fredericton, NB ; Ghorbani, A.A.

Intrusion detection is an effective approach for dealing with various problems in the area of network security. This paper presents a comparative study of using supervised probabilistic and predictive machine learning techniques for intrusion detection. Two probabilistic techniques Naive Bayes and Gaussian and two predictive techniques decision tree and random forests are employed. Different training datasets constructed from the KDD99 dataset are employed for training. The ability of each technique for detecting four attack categories (DoS,Probe,R2L and U2R) have been compared. The statistical results to show the sensitivity of each technique to the population of attacks in a dataset have also been reported. We compare the performance of the techniques and also investigate the robustness of each technique by calculating their standard deviations with respect to the detection rate of each attack category.

Published in:

Communication Networks and Services Research, 2007. CNSR '07. Fifth Annual Conference on

Date of Conference:

14-17 May 2007