Multi-level intrusion detection system (ML-IDS)

5th International Conference on Autonomic Computing, ICAC 2008

Volumn , Issue , 2008, Pages 131-140

  Al Nashif, Youssif ^a   Kumar, Aarthi Arun ^a   Hariri, Salim ^a   Qu, Guangzhi ^b   Luo, Yi ^c   Szidarovsky, Ferenc ^c  

^a UNIVERSITY OF ARIZONA   (United States)

^b Oakland University   (United States)

^c University of Arizona   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTONOMIC COMPUTING; NETWORK ATTACKS;

ALARM SYSTEMS; ARSENIC COMPOUNDS; BOOLEAN FUNCTIONS; COMPUTER CRIME; COMPUTER SYSTEMS; FUSION REACTIONS; INTERNET; LARGE SCALE SYSTEMS; SIGNAL DETECTION; TRAFFIC SURVEYS;

INTRUSION DETECTION;

EID: 51649087689     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICAC.2008.25     Document Type: Conference Paper

References (42)

1. S. Hariri, G. Qu, H. Chen, Y. Al-Nashif, M. Yousif, "Autonomic Network Security Management: Design and Evaluation", ACM Transactions on Autonomous and Adaptive Systems - Special Issue on Adaptive Learning in Autonomic Communication, 2007.
2. K. Ilgun. "USTAT: A Real-Time Intrusion Detection System for UNIX", Master Thesis, University of California, Santa Barbara, November 1992.
3. G. Vigna and R. Kemmerer, "NetStat: A Network-Based Intrusion Detection Approach", In Proceedings of the 14th Annual Information Theory: 50 Years of Discovery Computer Security Application Conference, Dec. 1998.
4. U. Lindqvist, P.A. Porras, "Detecting Computer and Network Misuse through the Production-Based Expert System Toolset (P-BEST)", In Proceedings of the 1999 IEEE Symposium on Security and Privacy. pp. 146-161.
5. Wenke Lee and Salvatore Stolfo, "Data mining approaches for intrusion detection", In Proceedings of the 7th USENIX Security Symposium, San Antonio, TX, 1998.
6. Richard P. Lippmann and Robert K. Cunningham, "Improving intrusion detection performance using keyword selection and neural networks", Computer Networks, 2000, 34:597-603.
7. Jianxiong Luo, "Integrating fuzzy logic with data mining methods for intrusion detection", Master's thesis, Department of Computer Science, Mississippi State University, 1999.
8. M. Joshi and V. Kumar, "Credos: Classification using ripple down structure (a case for rare classes)", In Proceedings of 19th International Conference on Data Engineering, Bangalore, India, 2003.
9. Ramesh Agarwal and Mahesh V. Joshi, "PNrule: A new framework for learning classifier models in data mining (a case-study in network intrusion detection)", Technical Report TR 00-015, Department of Computer Science, University of Minnesota, 2000.
10. Aleksander Lazarevic, Nitesh V. Chawla, Lawrence O. Hall, and Kevin W. Bowyer, "Smoteboost: Improving the prediction of minority class in boosting", Technical Report 2002-136, AHPCRC, 2002
11. Stefanos Manganaris, Marvin Christensen, Dan Zerkle, and Keith Hermiz, "A data mining analysis of rtid alarms", In Proceedings of the 2nd International Workshop on Recent Advances in Intrusion Detection RAID, West Lafayette, IN, 1999.
12. Mahesh V. Joshi, Vipin Kumar, and Ramesh C. Agarwal, "Evaluating boosting algorithms to classify rare classes: Comparison and improvements", In ICDM, pages 257-264, San Jose, CA, 2001
13. Ertoz, L., Eilertson, E., Lazarevic, A., Tan, P., Srivastava, J., Kumar, V., Dokas, P., "The MINDS - Minnesota Intrusion Detection System", Next Generation Data Mining, MIT Press, 2004.
14. Dorothy E. Denning, "An intrusion-detection model", IEEE Transactions on Software Engineering, SE-13:222-232, 1987.
15. Harold S. Javitz and Alfonso Valdes, "The nides statistical component: Description and justification". Technical report, Computer Science Laboratory, SRI International, 1993.
16. T.F. Lunt and R Jagannathan, "A Prototype Real-Time Intrusion-Detection Expert System" In Proceedings of the IEEE Symposium on Security and Privacy, 1988, pp. 18-21.
17. D. Anderson, T. F. Lunt, H. Javitz, A. Tamaru, and A. Valdes, "Detecting unusual program behavior using the statistical component of the next-generation intrusion detection expert system NIDES", Technical Report SRI-CSL-95-06, Computer Science Laboratory, SRI International, 1995.
18. P.A. Porras and P.G. Neumann, "EMERALD: Event Monitoring Enabling Responses to Anomalous Live Disturbances", In Proceedings of the National Information Systems Security Conference, 1997, pp. 353-365.
19. K. Sequeira, M. Zaki, "ADMIT: Anomaly-base Data Mining for Intrusions", Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, July 2002.
20. N. Ye, "A Markov Chain Model of Temporal Behavior for Anomaly Detection", Proceedings of the 2000 IEEE Systems, Man, and Cybernetics.
21. Stuart Staniford, James A. Hoagland, and Joseph M. McAlerney, "Practical automated detection of stealthy portscans", Journal of Computer Security, 10:105-136, 2002.
22. Eleazar Eskin, Andrew Arnold, Michael Prerau, Leonid Portnoy, and Sal Stolfo, "A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data", Data Mining for Security Applications, 2002.
23. Charu C. Aggarwal and Philip S. Yu, "Outlier detection for high dimensional data". In SIGMOD Conference, 2001.
24. R. Sekar, A. Gupta, J. Frullo, T. Shanbhag, A. Tiwari, H. Yang, and S. Zhou, "Specification based anomaly detection: A new approach for detecting network intrusions", In ACM Conference on Computer and Communications Security, 2002.
25. Shon, T. and Moon, J., "A hybrid machine learning approach to network anomaly detection" Inf. Sci. 177, 18 Sep. 2007, 3799-3821.
26. G. Qu, S. Hariri, and M. S. Yousif, "A new dependency and correlation analysis for features." IEEE Trans. Knowl. Data Eng., vol. 17, no. 9, 2005 pp. 1199-1207.
27. Y. Al-Nashif, G. Qu, H. Chen, S. Hariri, and A. Arun Kumar, "Autonomic Network Defense (AND) System: Design and Analysis", Submitted to IEEE Transactions on Secure and Dependable Computing.
28. H. Chen, Y. Al-Nashif, G. Qu, and S. Hariri, "Self-Configuration of Network Security", the 11th IEEE International EDOC Conference, Annapolis, Maryland U.S.A. 15-19 October 2007.
29. InSeon Yoo and Ulrich Ultes-Nitsche, "Towards Runtime Protocol Anomaly Detection and Verification", Proc. of the 1st International Conference on E-Business and Telecommunication Networks, Setubal, Portugal, 25-28 August, 2004.
30. RFC793, "Transmission control protocol", September 1981. DARPA Internet Program Protocol Specification.
31. Ke Wang, Janak J. Parekh, Salvatore J. Stolfo "Anagram: A Content Anomaly Detector Resistant To Mimicry Attack", In Proceedings of the Nineth International Symposium on Recent Advances in Intrusion Detection(RAID 2006)
32. Staniford-Chen, S., V. Paxson, and N. Weaver, "How to Own the Internet in Your Spare Time", In USENIX Security, 2002.
33. Christodorescu, M. and S. Jha. "Static Analysis of Executables to Detect Malicious Patterns", In USENIX Security Symposium, Washington, D.C. 2003.
34. Vargiya, R. and P. Chan., "Boundary Detection in Tokenizing Network Application Payload for Anomaly Detection", in ICDM Workshop on Data Mining for Computer Security (DMSEC), Melbourne, FL, 2003.
35. Bloom, B.H., "Space/time trade-offs in Hash Coding with Allowable Errors", Communications of the ACM, 1970. 13(7): p. 422-426.
36. IOS Firewall and Microsoft Windows Vista TCP Window Scaling, Document ID:71613, 2006-2007 Cisco Systems, Inc.
37. Network connectivity may fail when you try to use Windows Vista behind a firewall device, Article 1D:93443O, Sept. 26, 2007. Microsoft Corp.
38. Martin Roesch, "Snort - Lightweight Intrusion Detection for Networks", 13th Systems Administration Conference - LISA 1999.
39. Daniel Barbara, Ningning Wu, and Sushil Jajodia, "Detecting novel network intrusions using bayes estimators", In Proceedings of First SIAM Conference on Data Mining, Chicago, IL, 2001.
40. W. W. Cohen. "Fast effective rule induction", In Proceedings of the Twelfth International Conference on Machine Learning, Lake Tahoe, California, 1995
41. Arash Partow, http://www.partow.net/programming/hashfunctions/ #AvailableHashFunctions
42. Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer, 2007.