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

Anomaly detection in information streams without prior domain knowledge

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 $31
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)
Beigi, M.S. ; IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY, USA ; Chang, S.-F. ; Ebadollahi, S. ; Verma, D.C.

A key goal of information analytics is to identify patterns of anomalous behavior. Such identification of anomalies is required in a variety of applications such as systems management, sensor networks, and security. However, most of the current state of the art on anomaly detection relies on using a predefined knowledge base. This knowledge base may consist of a predefined set of policies and rules, a set of templates representing predefined patterns in the data, or a description of events that constitutes anomalous behavior. When used in practice, a significant limitation of information analytics is the effort that goes into defining and creating the predefined knowledge base and the need to have prior information about the domain. In this paper, we present an approach that can identify anomalies in the information stream without requiring any prior domain knowledge. The proposed approach simultaneously monitors and analyzes the data stream at multiple temporal scales and learns the evolution of normal behavior over time in each time scale. The proposed approach is not sensitive to the choice of the distance metric and hence is applicable in various domains and applications. We have studied the effectiveness of the approach using different data sets.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:55 ,  Issue: 5 )

Date of Publication:

Sept.-Oct. 2011

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.