Detecting known and novel network intrusions

IFIP International Federation for Information Processing

Volumn 201, Issue , 2006, Pages 258-270

  Bouzida, Yacine ^a   Cuppens, Frédéric ^b  

^a Mitsubishi Electric ITE TCL   (France)

^b ECOLE DES MINES DE NANTES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ACCESS CONTROL; DECISION TREES; LEARNING ALGORITHMS; LEARNING SYSTEMS; PERSONAL COMPUTING; SECURITY OF DATA; TREES (MATHEMATICS);

ANOMALY BASED INTRUSION DETECTION SYSTEMS; ANOMALY DETECTION; BUILDING DETECTION; CURRENT SIGNATURES; DETECTION SYSTEM; NETWORK INTRUSIONS; REAL TIME; UNKNOWN ATTACKS;

INTRUSION DETECTION;

EID: 33845518125     PISSN: 15715736     EISSN: None     Source Type: Book Series    
DOI: 10.1007/0-387-33406-8_22     Document Type: Article

References (7)

1. J. P. Anderson. Computer Security Threat Monitoring and Surveillance. Technical report, James. P. Anderson Co., Fort Washington, Pennsylvania, 1980.
2. Y. Bouzida and S. Gombault. Eigenconnections to Intrusion Detection. In 19th IFIP International Information Security Conference (SEC'2004), pages 241-258, Toulouse, France, August 2004. Kluwer Academic Publishers.
3. DARPA Intrusion Detection Evaluation. Available at: http://www.ll.mit.edu/IST/ideval/data/data_index.html, 1998.
4. C. Elkan. Results of the KDD'99 Classifier Learning. ACM SIGKDD, 1:63-64, 2000.
5. S. Hettich and S. D. Bay. The UCI KDD Archive. Available at: http://kdd.ics.uci.edu/, 1999.
6. W. Lee and S. Stolfo. A Framework for Constructing Features and Models for Intrusion Detection Systems. ACM Transactions on Information and System Security, 3(4), November 2000.
7. J. R. Quinlan. C4.5: Programs for machine learning. Morgan Kaufmann Publishers, 1993.