Applying machine learning techniques for detection of malicious code in network traffic

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

Volumn 4667 LNAI, Issue , 2007, Pages 44-50

  Elovici, Yuval ^a   Shabtai, Asaf ^a   Moskovitch, Robert ^a   Tahn, Gil ^a   Glezer, Chanan ^a  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

Feature selection; Machine learning; Malicious code; Network Service Provider (NSP)

Indexed keywords

ALGORITHMS; BAYESIAN NETWORKS; FEATURE EXTRACTION; NEURAL NETWORKS;

MALICIOUS CODE; NETWORK SERVICE PROVIDER (NSP);

LEARNING SYSTEMS;

EID: 38349051945     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74565-5_5     Document Type: Conference Paper

References (15)

1. NCSA Study, http://www.staysafeonline.info/pdf/safety_study_2005.pdf
2. Symantec Internet Security Threat Report (January-June 2004), www.symantec.com
3. The Danger of Spyware, Symantec Security Response (June 2003), http:// www.symantec.com
4. Symantec 2006 Security Report, http://www.symantec.com/specprog/ threatreport/ entwhitepaper_symantec_internet_security_threat_report_×
5. Tahan, G., Glezer, C., Elovici, Y.: eDare- Early Detection Alert and Response to Electronic Threats, Working Paper, Deutsche Telekom Labs at Ben Gurion University
6. Schultz, M., Eskin, E., Zadok, E., Stolfo, S.: Data Mining Methods for Detection of New Malicious Executables. In: Proc. of the IEEE Symposium on Security and Privacy, pp. 178-184(2001)
7. Abou-Assaleh, T., Cercone, N., Keselj, V., Sweidan, R.: N-gram based Detection of New Malicious Code. In: Proc. of the 28th Annual International Computer Software and Applications Conference (COMPSAC'04) (2004)
8. Kolter, J.Z., Maloof, M.A.: Learning to detect malicious exécutables in the wild. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 470-478. ACM Press, New York, NY (2004)
9. Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
10. Weka software, http://www.cs.waikato.ac.nz/ml/weka/
11. Pearl, J.: Fusion, propagation, and structuring in belief networks. Artificial Intelligence 29(3), 241-288 (1986)
12. Bishop, C.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)
13. Demuth, H., Beale, M.: Neural Network toolbox for use with Matlab. The Mathworks Inc., Natick, MA (1998)
14. Golub, T., Slonim, D., Tamaya, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, J., Caligiuri, M., Bloomfield, C., Lander, E.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531-537 (1999)
15. Bauer, E., Kohavi, R.: An empirical comparison of voting classification Algorithms. Bagging, Boosting, and Variants. Machine Learning 35, 1-38 (1999)