Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines

Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)

Volumn P 46, Issue , 2004, Pages 71-82

  Laskov, Pavel ^a   Schäfer, Christin ^a   Kotenko, Igor ^b  

^a FRAUNHOFER FIRST   (Germany)

^b ST PETERSBURG INSTITUTE FOR INFORMATICS AND AUTOMATION   (Russian Federation)

Author keywords

[No Author keywords available]

Indexed keywords

ANOMALY DETECTION; DATA MINING; MALWARE; NETWORK SECURITY; SUPPORT VECTOR MACHINES;

CENTER OF MASS; FEATURE SPACE; GEOMETRIC FRAMEWORK; MACHINE LEARNING TECHNIQUES; ONE-CLASS SUPPORT VECTOR MACHINE; SPHERE SUPPORT VECTORS; UNLABELED DATA; UNSUPERVISED ANOMALY DETECTION;

INTRUSION DETECTION;

EID: 85016684916     PISSN: 16175468     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Barbará, D., Couto, J., Jajodia, S., Popyack, L., und Wu, N.: ADAM: Detecting intrusions by data mining. In: Proc. IEEE Workshop on Information Assurance and Security. S. 11-16. 2001.
2. Denning, D.: An intrusion-detection model. IEEE Transactions on Software Engineering. 13:222-232. 1987.
3. Dowell, C. und Ramstedt, P.: The ComputerWatch data reduction tool. In: Proc. 13th National Computer Security Conference,. S. 99-108. 1990.
4. Eskin, E., Arnold, A., Prerau, M., Portnoy, L., und Stolfo, S.: Applications of Data Mining in Computer Security. chapter A geometric framework for unsupervised anomaly detection: detecting intrusions in unlabeled data. Kluwer. 2002.
5. Jagannathan, R., Lunt, T. F., Anderson, D., Dodd, C., Gilham, F., Jalali, C., Javitz, H. S., Neumann, P. G., Tamaru, A., und Valdes, A.: Next-generation intrusion detection expert system (NIDES). Technical report. Computer Science Laboratory, SRI International. 1993.
6. Lazarevic, A., Ertoz, L., Kumar, V., Ozgur, A., und Srivastava, J.: A comparative study of anomaly detection schemes in network intrusion detection,. In: Proc. SIAM Conf. Data Mining. 2003.
7. Liepins, G. und Vaccaro, H.: Intrusion detection: its role and validation. Computers and Security,. 11(4):347-355. 1992.
8. Noel, S., Wijesekera, D., und Youman, C.: Applications of Data Mining in Computer Security. chapter Modern intrusion detection, data mining, and degrees of attack guilt. Kluwer. 2002.
9. Portnoy, L., Eskin, E., und Stolfo, S.: Intrusion detection with unlabeled data using clustering. In: Proc. ACM CSS Workshop on Data Mining Applied to Security. 2001.
10. Porras, P. A. und Neumann, P. G.: Emerald: event monitoring enabling responses to anomalous live disturbances. In: Proc. National Information Systems Security Conference. S. 353-365. 1997.
11. Schölkopf, B., Mika, S., Burges, C., Knirsch, P., Müller, K.-R., Rätsch, G., und Smola, A.: Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks. 10(5):1000-1017. September 1999.
12. Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., und Williamson, R.: Estimating the support of a high-dimensional distribution. Neural Computation. 13(7):1443-1471. 2001.
13. Schölkopf, B. und Smola, A.: Learning with Kernels. MIT Press. Cambridge, MA. 2002.
14. Schölkopf, B., Smola, A., und Müller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation. 10:1299-1319. 1998.
15. Tax, D. und Duin, R.: Data domain description by support vectors. In: Verleysen, M. (Hrsg.), Proc. ESANN. S. 251-256. Brussels. 1999. D. Facto Press.
16. Vapnik, V.: The nature of statistical learning theory. Springer Verlag. New York. 1995.
17. Vapnik, V.: Statistical Learning Theory. Wiley. New York. 1998.
18. Valdes, A. und Skinner, K.: Adaptive, model-based monitoring for cyber attack detection. In: Proc. RAID 2000. S. 80-92. 2000.
19. Warrender, C., Forrest, S., und Perlmutter, B.: Detecting intrusions using system calls: alternative data methods. In: Proc. IEEE Symposium on Security and Privacy. S. 133-145. 1999.