Classification of computer intrusions using functional networks. A comparative study

ESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks

Volumn , Issue , 2007, Pages 579-584

  Alonso Betanzos, A ^a   Sanchez Marono N ^a   Carballal Fortes, F M ^a   Suarez Romero J ^a   Perez Sanchez B ^a  

^a UNIVERSITY OF A CORUÑA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

COMPARATIVE STUDIES; COMPUTER ATTACKS; COMPUTER SCIENTISTS; EXPONENTIAL INCREASE; FUNCTIONAL NETWORK; MACHINE LEARNING TECHNIQUES;

FEEDFORWARD NEURAL NETWORKS; INTRUSION DETECTION; LEARNING SYSTEMS;

COMPUTER CRIME;

EID: 67349235269     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. A. Chebrolu, A. Abraham, and J. Thomas, Feature Deduction and Ensemble Design of Intrusion Detection Systems. Computers & Security, 24:4, 295-307, 2005.
2. O. Depren, M. Topallar, E. Anarim and M. Kemal, An Intelligent Intrusion Detection System (IDS) for Anomaly and Misuse Detection in Computer Networks, Expert Systems with Applications, 29:713-722, 2005.
3. M. Fugate and J.R. Gattiker, Computer Intrusion Detection with Classi-cation and Anomaly Detection, using SVMs, International Journal of Pattern Recognition and Arti-cial Intelligence, 17: 441-458, 2003.
4. M. Fugate and J.R. Gatikker, Anomaly Detection Enhanced Classi-cation in Computer Intrusion Detection. Applications of Support Vector Machines, Int. Conf. Pattern Recognition and Machine Learning, pp. 186-197, 2002.
5. KDD Cup 1999 Data. [Last access: 20th october 2005].
6. R. P. Lipmann, D. Fried, I. Graf, J. Haines, K. Kendall, D. McClung, D. Weber, S. Webster, D. Wyschogrod, R. Cunningham, and M. Zissman, Evaluating intrusion detection systems. In the 1998 DARPA off-line intrusion detection evaluation. Proc. DARPA Information Survivability Conf. & Exposition, 2000.
7. S. Mukkamala, A. Sung, and A. Abraham, Intrusion Detection using an ensemble of intelligent paradigms. Journal of Network and Computer Applications, 28:167-182, 2005.
8. S. Mukkamala, A. Sung, Feature Ranking and Selection for Intrusion Detection Systems Using Support Vector Machines. In Digital Forensic Research Workshop, 2002.
9. S. Stolfo, W. Fan, W. Lee, A. Prodomidis and R.K. Chan, Cost-based modeling for fraud and intrusion detection: Results from the JAM project. In DARPA Information Survivability Conf., 2000.
10. E. Castillo and A. Cobo and J.M. Gutierrez and E. Pruneda. Functional Networks with Applications, Kluwer Academic Publishers, 1998.
11. S. Axelsson, The base-rate fallacy and its implications for the di±culty of intrusion detection. Proceedings of the 6th ACM Conference on computer and Communications security (CCS 1999), pages 1-7, ACM press, 1999.
12. M. Sabhnani, Why machine learning algorithms fall in misuse detection on KDD Intrusion detection data set. Journal of Intelligent Data Analysis, 8:403-445, 2004.