Network intrusion detection by a multi-stage classification system

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3077, Issue , 2004, Pages 324-333

  Cordelia, Luigi Pietro ^a   Limongiello, Alessandro ^b   Sansone, Carlo ^a  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

^b UNIVERSITY OF SALERNO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER CRIME; EXPERT SYSTEMS; MERCURY (METAL); UNCERTAINTY ANALYSIS;

CLASSIFICATION SYSTEM; DECISION PROCESS; DIFFERENT SERVICES; EXPERIMENTAL ANALYSIS; INTRUSION DETECTION SYSTEMS; MULTI-EXPERT SYSTEMS; NETWORK INTRUSION DETECTION; NUMBER OF FALSE ALARMS;

INTRUSION DETECTION;

EID: 27244448490     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Article

References (14)

1. S. Axelsson, Research in Intrusion Detection Systems: A Survey, TR 98-17, Chalmers University of Technology, 1999.
2. th National Information Security Conference, pp. 194-204, 1995.
3. th USENIX Security Symposium, Aug. 26-29 1999, Washington DC.
4. T. Lane, C.E. Brodley, "Temporal Sequence learning and data reduction for anomaly detection", ACM Trans. on Inform. and System Security, vol. 2, no. 3, pp. 295-261, 1999.
5. K. Kendall, A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems, Master's Thesis, Massachusetts Institute of Technology, 1998.
6. G. Giacinto, F. Roli, L. Didaci, "Fusion of multiple classifiers for intrusion detection in computer networks", Pattern Recognition Letters, vol. 24, pp. 1795-1803, 2003.
7. C. Elkan, "Results of the KDD99 classifier learning", ACM SIGKDD Explorations 1, 63-64, 2000.
8. S. C. Lee, D.V. Heinbuch, "Training a neural Network based intrusion detector to recognize novel attack", IEEE Trans. Syst, Man., and Cybernetic, Part-A, vol. 31, pp. 294-299, 2001.
9. M. Fugate, J.R. Gattiker, "Computer Intrusion Detection with Classification and Anomaly Detection, using SVMs", International Journal of Pattern Recognition and artificial Intelligence, vol. 17, no. 3, pp. 441-458, 2003.
10. G. Giacinto, F. Roli, L. Didaci, "A Modular Multiple Classifier System for the Detection of Intrusions", Lecture Notes in Computer Science vol. 2709, pp. 346-355, 2003.
11. J. Beale, J.C. Foster, Snort 2.0 Intrusion Detection, Syngress Publishing, Inc., Rockland, MA, 2003.
12. L.P. Cordella, C. Sansone, F. Tortorella, M. Vento, C. De Stefano, "Neural Networks Classification Reliability", in C.T. Leondes (ed.). Academic Press theme volumes on Neural Network Systems, Techniques and Applications. Academic Press, 5, pp. 161-199, 1998.
13. C. Sansone, F. Tortorella, M.Vento, "A Classification Reliability Driven Reject Rule for Multi-Expert Systems", International Journal of Pattern Recognition and Artificial Intelligence, vol. 15, no. 6, pp. 885-904, 2001.
14. W. Lee, S.J. Stolfo, "A framework for constructing features and models for intrusion detection systems", ACM Transactions on Inform. System Security, vol. 3, no. 4, pp. 227-261, 2000.