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

Challenges of Machine Learning Based Monitoring for Industrial Control System Networks

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)
Mantere, M. ; VTT Tech. Res. Centre of Finland, Espoo, Finland ; Uusitalo, I. ; Sailio, M. ; Noponen, S.

Detecting network intrusions and anomalies in industrial control systems is growing in urgency. Such systems used to be isolated but are now being connected to the outside world. Even in the case of isolated networks, privileged users may still present various threats to the system, either accidentally or intentionally. Also malfunctions in devices may cause anomalous traffic. Anomaly detection based network monitoring and intrusion detection systems could be capable of discerning normal and aberrant traffic in industrial control systems, detecting security incidents in an early phase. In this paper we discuss the challenges for such a monitoring system. One of the challenges is which features best differentiate between anomalous and normal behaviour. In the analysis, special focus is placed on this selection.

Published in:

Advanced Information Networking and Applications Workshops (WAINA), 2012 26th International Conference on

Date of Conference:

26-29 March 2012

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.