A study on classification techniques for network intrusion detection

Proceedings of the IASTED International Conference on Networks and Communication Systems 2006

Volumn 2006, Issue , 2006, Pages 352-367

  Kaplantzis, Sophia ^a   Mani, Nallasamy ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

Artificial neural networks; K means classifier; Network intrusion detection; Support vector machines

Indexed keywords

COMPUTER SYSTEMS; INTRUSION DETECTION; NETWORK SECURITY; NEURAL NETWORKS; SUPPORT VECTOR MACHINES;

MALICIOUS ATTACKS; SOFTWARE BUGS;

CLASSIFICATION (OF INFORMATION);

EID: 34047165837     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. "Symantec internet security threat report highlights rise in threats to confidential information (symantec.com)," Accessed: 2005, May 10. Available: http://www.prdomain.com/companies/s/symantec/newsreleases/200503mar/ symantec_internet_20050321.htm.
2. R. Durst, T. Champion, B. Witten, E. Miller, and L. Spagnuolo, "Testing and evaluating computer intrusion detection systems," Commun. ACM, vol. 42, no. 7, pp. 53-61, 1999.
3. A. Sung and S. Mukkamala, "Identifying important features for intrusion detection using support vector machines and neural networks," in Symposium on Applications and the Internet, pp. 209-216, 2003.
4. "A tutorial on clustering algorithms," Accessed: 2004, October 1. Available: http://www.elet.polimi.it/upload/matteucc/Clustering/ tutorial_html/kmeans.html.
5. S. Kaplantzis, "Classification techniques for network intrusion detection," 4th year thesis project, Monash University, 2004.
6. P. Picton, Neural networks. Grassroots series (Palgrave (Firm)), Basingstoke: Palgrave, 2nd ed., 2000.
7. V. N. Vapnik, Statistical learning theory. Adaptive and learning systems for signal processing, communications, and control, New York: Wiley, 1998.
8. D. Lai and N. Mani, "Support vector machines and linear stationary models," conversion report, Monash University, 2003.
9. D. Lai, "Welcome to daniel lai," Accessed: 2004, September 21. Available: http://www-personal.monash.edu.au/~dlai.
10. "Darpa intrusion detection evaluation (mit lin-coln laboratory)," Accessed: 2004, October 1. Available:http://www.11.mit.edu/ IST/ideval/.
11. P. Jeong and S. Nedevschi, "Efficient and robust classification method using combined feature vector for lane detection," Circuits and Systems for Video Technology, IEEE Transactions on, vol. 15, no. 4, pp. 528-537, 2005.
12. W. Allen and G. Marin, "On the self-similarity of synthetic traffic for the evaluation of intrusion detection systems," in Symposium on Applications and the Internet, pp. 242-248, 2003.
13. A. Bivens, C. Palagiri, R. Smith, B. Szymanski, and M. Membrechts, "Network based intrustion detection using neural networks," 2002.
14. C. Manikopoulos and S. Papavassiliou, "Network intrusion and fault detection: a statistical anomaly approach," Communications Magazine, IEEE, vol. 40, no. 10, pp. 76-82, 2002.
15. S. Mukkamala and A. Sung, "A comparative study of techniques for intrusion detection," in Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE International Conference on, pp. 570-577, 2003.