Cart (Loading....) | Create Account
Close category search window

Stochastic learning automata-based time series analysis for network anomaly 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

3 Author(s)
Yasami, Y. ; Comput. Eng. Dept., Amirkabir Univ. of Technol. (AUT), Tehran ; Mozaffari, S.P. ; Khorsandi, S.

The main drawback of traditional intrusion detection systems makes anomaly detection systems an active research area. In this paper we introduce a novel network-based anomaly detection approach using stochastic learning automata. The paper main objective is to construct a network-based statistical anomaly detection system capable of classifying the ensemble network broadcast traffic as normal or abnormal. For this purpose the approach constructs a learning automaton from time series data of purified network broadcast traffic in learning process. Time series data of observed network broadcast traffic are compared by the normal model and any deviation from it is marked as abnormal. This approach is novel in that apply stochastic learning automaton with a special reinforcement scheme to the problem of network anomaly detection and presents an online high precision network-based anomaly detection system using broadcast traffic time series data.

Published in:

Telecommunications, 2008. ICT 2008. International Conference on

Date of Conference:

16-19 June 2008

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.