Graph-based anomaly detection

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2003, Pages 631-636

  Noble, Caleb C ^a   Cook, Diane J ^a  

^a The University of Texas at Arlington   (United States)

Author keywords

Anomaly detection; Data mining; Graph regularity

Indexed keywords

ANOMALY DETECTION; FRAUD DETECTION; GRAPH-BASED; NETWORK INTRUSION DETECTION; REAL-WORLD NETWORKS; VALUE DATA;

DATA MINING;

INTRUSION DETECTION;

EID: 12244268431     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/956750.956831     Document Type: Conference Paper

References (6)

1. Cook, D. J. and Holder, L. B. Graph-Based Data Mining. IEEE Intelligent Systems, 15 (2), pages 32-41, 2000.
2. Lee, W. and Xiang, D. Information-Theoretic Measures for Anomaly Detection. Proceedings of The 2001 IEEE Symposium on Security and Privacy, Oakland, CA, May 2001.
3. Maxion, R. A. and Tan, K. M. C. Benchmarking Anomaly-Based Detection Systems. International Conference on Dependable Systems and Networks, pages 623-630, New York, New York; 25-28 June 2000.
4. Miller, G. A. Note on the Bias of Information Estimates. Information Theory in Psychology: Problems and Methods, Free Press, 1955.
5. Rissanen, J. Stochastic Complexity in Statistical Inquiry. World Scientific Publishing Company, 1989.
6. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html