Next-generation misuse and anomaly prevention system

Lecture Notes in Business Information Processing

Volumn 19, Issue , 2009, Pages 117-129

  Bringas, Pablo García ^a   Penya, Yoseba K ^a  

^a UNIVERSITY OF DEUSTO   (Spain)

Author keywords

Anomaly detection; Bayesian networks; Data mining; Intrusion prevention; Machine learning; Misuse detection

Indexed keywords

COMPUTER CRIME; DISTRIBUTED PARAMETER NETWORKS; INFERENCE ENGINES; INFORMATION MANAGEMENT; INFORMATION SYSTEMS; INTELLIGENT NETWORKS; INTERNET; INTRUSION DETECTION; ROBOT LEARNING; SPEECH ANALYSIS; SPEECH RECOGNITION;

ANOMALY DETECTION; INTRUSION PREVENTION; KNOWLEDGE MODELS; MACHINE LEARNING; MISUSE DETECTION; NETWORK ATTACKS; NETWORK INTRUSION DETECTION SYSTEMS; NETWORK PACKETS; PREVENTION SYSTEMS; TRAINING PROCESS;

BAYESIAN NETWORKS;

EID: 64549160247     PISSN: 18651348     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-00670-8_9     Document Type: Article

References (25)

1. Internet System Consortium, Internet Domain Survey, http://www.isc.org
2. Kabiri, P., Ghorbani, A.A.: Research on intrusion detection and response: A survey. Int. J. on Information Security 1(2), 84-102 (2005)
3. Alipio, P., Carvalho, P., Neves, J.: Using CLIPS to Detect Network Intrusion. In: Pires, F.M., Abreu, S.P. (eds.) EPIA 2003. LNCS, vol. 2902, pp. 341-354. Springer, Heidelberg (2003)
4. Vigna, G., Eckman, S., Kemmerer, R.: The STAT tool suite. In: DARPA Information Survivability Conference and Exposition, vol. 2, p. 1046. IEEE Press, Los Alamitos (2000)
5. Kantzavelou, I., Katsikas, S.: An attack detection system for secure computer systems outline of the solution. In: 13th International IFIP TC11 Conference on Information Security, pp. 123-135 (1997)
6. Mukkamala, S., Sung, A., Abraham, A.: Intrusion detection using an ensemble of intelligent paradigms. J. of Network and Computer Applications 28, 167-182 (2005)
7. Doyle, J., Kohane, I., Long,W., Shrobe, H., Szolovits, P.: Event recognition beyond signature and anomaly. In: 2001 IEEEWorkshop on Information Assurance and Security, pp. 170-174 (2001)
8. Kim, D., Nguyen, H., Park, J.: Genetic algorithm to improve svm-based network intrusion detection system. In: 19th International Conference on Advanced Information Networking and Applications, vol. 2, pp. 155-158 (2005)
9. Chavan, S., Shah, K., Dave, N., Mukherjee, S., Abraham, A., Sanyal, S.: Adaptative neurofuzzy intrusion detection systems. In: 2004 International Conference on Information Technology: Coding and Computing, vol. 1, pp. 70-74 (2004)
10. Helmer, G., Wong, J., Honavar, V., Miller, L., Wang, Y.: Lightweight agents for intrusion detection. J. of Systems and Software 67, 109-122 (2003)
11. Lazarevic, A., Ertoz, L., Kumar, V., Ozgur, A., Srivastava, J.: A comparative study of anomaly detection schemes in network intrusion detection. In: SIAM International Conference on Data Mining (2003)
12. Skinner, K., Valdes, A.: Adaptive, Model-based Monitoring for Cyber Attack Detection. In: Debar, H., M'e, L., Wu, S.F. (eds.) RAID 2000. LNCS, vol. 1907, pp. 80-92. Springer, Heidelberg (2000)
13. Singhal, A., Jajodia, S.: Data warehousing and data mining techniques for intrusion detection systems. Int. J. on Information Security 1(2), 149-166 (2006)
14. Brugger, T.: Data Mining Methods for Network Intrusion Detection. PhD thesis. University of California Davis (2004)
15. Roesch, M.: SNORT: Lightweight intrusion detection for networks. In: 13th Systems Administration Conference, pp. 229-238 (1999)
16. Crothers, T.: Implementing Intrusion Detection Systems: A Hands-On Guide for Securing the Network. John Wiley & Sons, Chichester (2002)
17. Metasploit: Exploit research (2006), http://www.metasploit.org
18. Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. In: Adaptive Computation and Machine Learning, 2nd edn. MIT Press, Cambridge (2001)
19. Murphy, K.: An introduction to graphical models. Technical report. Intel Research, Intel Corporation (2001)
20. Castillo, E., Gutierrez, J.M., Hadi, A.S.: Expert Systems and Probabilistic Network Models. Springer, Heidelberg (1997)
21. Estevez-Tapiador, J., Garcia-Teodoro, P., Diaz-Verdejo, J.: Stochastic protocol modelling for anomaly based network intrusion detection. In: 1st IEEE International Workshop on Information Assurance, pp. 3-12 (2003)
22. Kruegel, C., Vigna, G.: Anomaly detection of web-based attacks. In: 10th ACM Conference on Computer and Communications Security, pp. 251-261 (2003)
23. Ghahramani, Z.: Learning Dynamic Bayesian Networks. Adaptive Processing of Sequences and Data Structures. In: International Summer School on Neural Networks, pp. 168-197. Springer, London (1998)
24. Snort: The facto standard for intrusion detection and prevention, http://www.snort.org/
25. Lee, W., Stolfo, S., Chan, P., Eskin, E., Fan, W., Miller, M., Hershkop, S., Zhang., J.: Real time data mining-based intrusion detection. In: 2nd DARPA Information Survivability Conference and Exposition, vol. 1, pp. 89-100 (2001)