Optimized clustering for anomaly intrusion detection

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2637, Issue , 2003, Pages 576-581

  Oh, Sang Hyun ^a   Lee, Won Suk ^a  

^a Yonsei University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; ONLINE SYSTEMS; ALGORITHMS; CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; INTRUSION DETECTION;

ANOMALY INTRUSION ALGORITHM; CLUSTERING ALGORITHM; ONLINE TRANSACTIONS;

ALGORITHMS; DATA MINING;

ANOMALOUS BEHAVIOR; ANOMALY INTRUSION DETECTION; CLUSTERING METHODS; COMMON KNOWLEDGE; CONVENTIONAL CLUSTERING; DATA SIMILARITY; ONLINE TRANSACTION; OPTIMAL VALUES;

EID: 7444249623     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-36175-8_57     Document Type: Conference Paper

References (6)

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2. Phillip A. Porras and Peter G. Neumann, "EMERALD: Event Monitoring Enabling Responses to Anomalous Live Disturbances," 20th NISSC, October 1997.
3. Martin Ester, Hans-Peter Kriegel, Sander, Michael Wimmer, Xiaowei Xu, "Incremental Clustering for Mining in a Data Warehousing Environment", Proceedings of the 24th VLDB Conference, New York, USA, 1998.
4. Tian Zhang, Raghu Ramakrishnan, and Miron Livny, "Birch: An Efficient data clustering method for very large databases," Proceedings for the ACM SIGMOD Conference on Management of Data, Montreal, Canada, June 1996.
5. Sang Hyun Oh and Won Suk Lee, "Clustering Normal User Behavior for Anomaly Intrusion Detection," Proceeding of the IASTED International Conference on Applied Modeling and Simulation, Cambridge, MA, USA, November 4-6, 2002.
6. Sun Microsystems. SunShield Basic Security Module Guide.