By Topic

Method for network anomaly detection based on Bayesian statistical model with time slicing

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

4 Author(s)
Tao Liu ; Xian Univ. of Sci. & Technol., Xian ; Ailing Qi ; Yuanbin Hou ; Xintan Chang

A method combining Bayesian statistical model with time slicing function is investigated to detect network anomaly. On the basis of analyzing Bayesian theory and rules of network traffic changing with time, the advantages of Bayesian theorem in solving uncertain problems were combined with the function whose network traffic changes with time. The purpose was to establish anomaly intrusion detection model for the network activity so as to determine the occurrence of network anomaly by discovering the relationship among mass events and classifying network system behavior. Simulation experimental results show that anomaly behavior is effectively detected by the method.

Published in:

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Date of Conference:

25-27 June 2008