By Topic

Towards data mining temporal patterns for anomaly intrusion detection systems

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

7 Author(s)
Sengupta, S. ; Inst. of Technol., State Univ. of New York, Utica, NY ; Andriamanalimanana, B. ; Card, S.W. ; Kadam, P.
more authors

A reasonably light-weight host and net-centric network IDS architecture model is indicated. The model is anomaly based on a state-driven notion of "anomaly". Therefore, the relevant distribution function need not remain constant; it could migrate from states to states without any a priori warning so long as its residency time at a next steady state is sufficiently long to make valid observations there. Only those intrusion events (basically DOS and DDOS variety) capable of triggering anomalous streams of attacks/response both near and/or far of target monitoring point(s) are considered at the first level of detection. At the next level of detection, the filtered states could be fine-combed in a batch mode to mine unacceptable strings of commands or known attack signatures

Published in:

Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2003. Proceedings of the Second IEEE International Workshop on

Date of Conference:

8-10 Sept. 2003