We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Context-based Profiling for Anomaly Intrusion Detection with Diagnosis

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)
Salem, B. ; CNRS, Univ. d''Artois, Lens ; Karim, T.

Anomaly detection approaches are generally efficient in detecting new attacks. However, they fail in providing any further information regarding the nature of attacks. The first contribution of this paper is to equip an anomaly detection approach with a diagnosis module that classifies anomaly approach outputs in one among well known attack categories. The second contribution concerns a context-based definition of normal network traffic profiles. We provide experimental studies showing for instance that considering normal profile for each service provides better results than considering a unique global normal profile.

Published in:

Availability, Reliability and Security, 2008. ARES 08. Third International Conference on

Date of Conference:

4-7 March 2008