By Topic

Real-time anomaly detection using a nonparametric pattern recognition approach

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)
Lankewicz, L. ; Dept. of Comput. Sci., Tulane Univ., New Orleans, LA, USA ; Benard, M.

Obstacles to achieving anomaly detection in real time include the large volume of data associated with user behavior and the nature of that data. The paper describes preliminary results from a research project which is developing a new approach to handling such data. The approach involves nonparametric statistical methods which permits considerable data compression and which supports pattern recognition techniques for identifying user behavior. This approach applies these methods to a combination of measurements of resource usage and structural information about the behavior of processes. Preliminary results indicate that both accuracy and real time response can be achieved using these methods

Published in:

Computer Security Applications Conference, 1991. Proceedings., Seventh Annual

Date of Conference:

2-6 Dec 1991