By Topic

A neural network component for an intrusion detection system

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
$33 $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)
H. Debar ; CSEE/DCI, Les Ulis, France ; M. Becker ; D. Siboni

An approach toward user behavior modeling that takes advantage of the properties of neural algorithms is described, and results obtained on preliminary testing of the approach are presented. The basis of the approach is the IDES (Intruder Detection Expert System) which has two components, an expert system looking for evidence of attacks on known vulnerabilities of the system and a statistical model of the behavior of a user on the computer system under surveillance. This model learns the habits a user has when he works with the computer, and raises warnings when the current behavior is not consistent with the previously learned patterns. The authors suggest the time series approach to add broader scope to the model. They therefore feel the need for alternative techniques and introduce the use of a neural network component for modeling user's behavior as a component for the intrusion detection system

Published in:

Research in Security and Privacy, 1992. Proceedings., 1992 IEEE Computer Society Symposium on

Date of Conference:

4-6 May 1992