Learning Rules for Anomaly Detection of Hostile Network Traffic

I
ICDM
2003
Third IEEE International Conference on Data Mining (ICDM'03)
Abstract - Learning Rules for Anomaly Detection of Hostile Network Traffic

	This Article
	Subscribers, please Login Purchase article: $19 PDF IEEE Xplore Subscribers RSS feed
	Share
	Email this Article to a friend
	Bibliographic References
	ASCII Text BibTex RefWorks Procite/RefMan
	Add to:
	Digg Furl Spurl Blink Simpy Google Del.icio.us Y!MyWeb
	Search
	Similar Articles Articles by Matthew V. Mahoney Articles by Philip K. Chan

Third IEEE International Conference on Data Mining (ICDM'03)

Melbourne, Florida

November 19-November 22

ISBN: 0-7695-1978-4

	ASCII Text	x
	Matthew V. Mahoney, Philip K. Chan, "Learning Rules for Anomaly Detection of Hostile Network Traffic," Data Mining, IEEE International Conference on, pp. 601, Third IEEE International Conference on Data Mining (ICDM'03), 2003.

	BibTex	x
	@article{ 10.1109/ICDM.2003.1250987, author = {Matthew V. Mahoney and Philip K. Chan}, title = {Learning Rules for Anomaly Detection of Hostile Network Traffic}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1978-4}, pages = {601}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250987}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Learning Rules for Anomaly Detection of Hostile Network Traffic SN - 0-7695-1978-4 SP EP A1 - Matthew V. Mahoney, A1 - Philip K. Chan, PY - 2003 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Matthew V. Mahoney, Florida Institute of Technology, Melbourne

Philip K. Chan, Florida Institute of Technology, Melbourne

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250987

We introduce an algorithm called LERAD that learns rules for finding rare events in nominal time-series data with long range dependencies. We use LERAD to find anomalies in network packets and TCP sessions to detect novel intrusions. We evaluated LERAD on the 1999 DARPA/Lincoln Laboratory intrusion detection evaluation data set and on traffic collected in a university departmental server environment.

Citation:

Matthew V. Mahoney, Philip K. Chan, "Learning Rules for Anomaly Detection of Hostile Network Traffic," icdm, pp.601, Third IEEE International Conference on Data Mining (ICDM'03), 2003

Peer Review Notice | Give Us Feedback

Usage of this product signifies your acceptance of the Terms of Use.