Journal of Engineering and Applied Sciences

Year: 2011
Volume: 6
Issue: 1
Page No. 79 - 90

Mobile Agents for Intrusion Detection System Based on A New Anomaly Approach

Authors : Farah Barika Ktata, Nabil El Kadhi and Khaled Ghedira

References

Anderson, J.P., 1980. Computer security threat monitoring and surveillance. Technical Report, James P. Anderson Company, Fort Washington, PA., USA., February 26, 1980.

Ando, R., Y. Kadobayashi and Y. Shinoda, 2007. Asynchronous pseudo physical memory snapshot and forensics on paravirtualized VMM using split kernel module. Lecture Notes Comput. Sci., 4817: 131-143.
CrossRef  |  

Androulidakis, G., V. Chatzigiannakis, M. Grammatikou and F. Stamatelopoulos, 2004. Network flow-based anomaly detection of DDoS attacks. Proceedings of Trans-European Research and Education Networking Association, June 2004, Rhodes, Greece, pp: 1-3.

Barika, F., N. El-Kadhi and K. Ghedira, 2003. Intelligent and mobile agent for intrusion detection system: IMA-IDS. Proceedings of 3rd International Conference of Information and Communication Technology, November 2003, Egypt, pp: 1-8.

Campagne, J.C. and A. Cardon, 2003. Artificial emotions for robots using massive multi-agent systems. Proceedings of the Social Intelligence Design International Conference, London.

Cardon, A., 2001. A distributed multi-agent system for the self-evaluation of dialogs. New Front. Artif. Intell., 2253: 43-50.
CrossRef  |  

Cardon, A., J.C. Campagne and M. Camus, 2005. A self-adapting system generating intentional behavior and emotions. Second GSFC/IEEE WRAC 2005: Workshop on Radical Agent Concept, NASA Goddard Space Flight Center.

Das, K., 2001. Protocol anomaly detection for network-based intrusion detection. SANS Institute, GSEC Practical Assignment Version 1.2f, http://www.sans.org/reading_room/whitepapers/detection/protocol-anomaly-detection-network-based-intrusion-detection_349.

Deeter, K., K. Singh, S. Wilson, L. Filipozzi and S. Vuong, 2004. APHIDS: A mobile agent-based programmable hybrid intrusion detection system. Mobility Aware Technol. Appl., 3284: 244-253.
CrossRef  |  

Denning, D.E., 1987. An intrusion-detection model. IEEE Trans. Software Eng., SE-13: 222-232.
CrossRef  |  

Deshpande, S., M. Thottan, T.K. Ho and B. Sikdar, 2006. A statistical approach to anomaly detection in interdomain routing. Proceedings of the 3rd International Conference on Broadband Communications, Networks and Systems, Oct. 1-5, San Jose, CA. USA., pp: 1-10.

Durgin, N.A. and P.C. Zhang, 2005. Profile-based adaptive anomaly detection for network Security. Sandia National Laboratories Technical Report, SAND2005-7293, November 2005.

Eskin, E., A. Arnold, M. Prerau, L. Portnoy and S. Stolfo, 2002. A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data. In: Applications of Data Mining in Computer Security, Barbara, D. and S. Jajodia (Eds.). Kluwer Academic Publishers, Boston.

Forrest, S., S.A. Hofmeyr, A. Somayaji and T.A. Longstaff, 1996. A sense of self for Unix processes. Proceedings of the IEEE Symposium on Securiry and Privacy, May 6-8, 1996, Oakland, CA. USA., pp: 120-128.

Gao, J., H. Cheng and P.N. Tan, 2006. A novel framework for incorporating labeled examples into anomaly detection. Proceedings of the 6th SIAM International Conference on Data Mining, April 2006, Bethesda, MD, pp: 594-597.

Gong, F., 2003. Deciphering detection techniques: Part II anomaly-based intrusion detection. White Paper, McAfee Network Security Technologies Group, March 2003.

Hawkins, S., H. He, G. Williams and R.A. Baxter, 2002. Outlier detection using replicator neural networks. Proceedings 4th International Conference on Data Warehousing and Knowledge Discovery, Sept. 4�6, Aix-en-Provence, France, pp: 113-123.

Honavar, V., L. Miller and J. Wong, 1998. Distributed knowledge networks. Proceedings of the IEEE Information Technology Conference, Sept. 1-3, Syracuse, New York, USA., pp: 87-90.

Javitz, H.S. and A. Valdes, 1994. The NIDES statistical component: Description and justifi-cation. Technical Report, Computer Science Laboratory, SRI International, Menlo Park, California.

Kim, D.S., H.N. Nguyen, S.Y. Ohn and J.S. Park, 2005. Fusions of GA and SVM for anomaly detection in intrusion detection system. Adv. Neural Networks ISNN., 3498: 415-420.
CrossRef  |  

Kumar, S. and E. Spafford, 1995. A software architecture to support misuse intrusion detection. Technical Report, Department of Computer Sciences, Purdue University, (CSD-TR-95-009).

Kumar, V., J. Srivastava and A. Lazarevic, 2005. Managing Cyber Threats: Issues, Approaches and Challenges. 1st Edn., Vol. 5, Springer, New York, ISBN: 9780387242309, pp: 330.

Lange, D.B. and M. Oshima, 1999. Seven good reasons for mobile agents. Commun. ACM., 42: 88-89.
CrossRef  |  Direct Link  |  

Lazarevic, A., L. Ertoz, V. Kumar, A. Ozgur and J. Srivastava, 2003. A comparative study of anomaly detection schemes in network intrusion detection. Proceedings of the 3rd SIAM International Conference on Data Mining, http://www.citeulike.org/user/horeis/article/549060.

Lee, W., S.J. Stolfo and K. Mok, 1999. Data mining in work ow environments: Experiences in intrusion detection. Proceedings of the Conference on Knowledge Discovery and Data Mining (KDD-99).

Li, W., 2004. Using genetic algorithm for network intrusion detection. Proceedings of the United States Department of Energy Cyber Security Group 2004 Training Conference, May 24-27, 2004, Kansas City, Kansas, USA., pp: 1-8.

Lingxi, P., L. Tao, L. Xiaojie, C. Yuefeng, L. Caiming and L. Sunjun, 2007. An immune system-inspired paradigm for anomaly detection. J. Comput. Theoret. Nanosci., 4: 1394-1398.
CrossRef  |  

Mariani, L. and F. Pastore, 2008. Automated identification of failure causes in system Logs. Proceedings of the 19th International Symposium on Software Reliability Engineering, Oct. 13-15, Washington, DC. USA., pp: 117-126.

Minsky, M., 2006. The Emotion Machine. Simon and Schuster, New York.

Puttini, R.S., Z. Marrakchi and L. Me, 2003. A bayesian classification model for real-time intrusion AIP Conf. Proc., 659: 150-162.
CrossRef  |  

Ranum, M.J., 2001. Experiences benchmarking intrusion detection systems. NFR Security.

Salem, O., S. Vaton and A. Gravey, 2007. A novel approach for anomaly detection over high-speed. Proceedings of the EC2ND : European Conference on Computer Network Defense, Heraklion, Greece, October 2007.

Scholkopf, B., R. Williamson, A. Smola, J. Shawe-Taylor and J. Platt, 2000. Support vector method for novelty detection. Adv. Neural Inform. Proc. Syst., Vol. 12.

Sekar, R., A. Gupta, J. Frullo, T. Shanbhag, A. Tiwari, H. Yang and S. Zhou, 2002. Specifi-cation based anomaly detection: A new approach for detecting network intrusions. Proceedings of the 9th ACM Conference on Computer and Communications Security, Nov. 18-22, Washington, DC, USA., pp: 265-274.

Shanbhag, S. and T. Wolf, 2008. Massively parallel anomaly detection in online network measurement. Proceedings of 17th IEEE International Conference on Computer Communications and Networks, Aug. 3-7, St. Thomas, US Virgin Islands, pp: 1-6.

Siris, V.A. and F. Papagalou, 2004. Application of anomaly detection algorithms for detecting SYN fooding attacks. Proceedings of the Global Communications Conference, November. 29-December. 3, 2004. Dallas, TX., pp: 2050-2054.

Specht, S.M. and R.B. Lee, 2004. Distributed denial of service: Taxonomies of attacks, tools and countermeasures. Proceedings of the 17th International Conference on Parallel and Distributed Computing System, September 15-17, 2004, San Francisco, CA., USA., pp: 543-550.

Spinellis, D. and D. Gritzalis, 2002. Panoptis: Intrusion detection using a domain-specific language. J. Comput. Security, 10: 159-176.
Direct Link  |  

Sun, H.W., K.Y. Lam, S.L. Chung, M. Gu and J.G. Sun, 2004. Grid and cooperative computing. Proceedings of the 3rd International Conference Wuhan, China, October 21-24, 2004.

Taylor, C. and F. Jim, 2001. NATE-network analysis of anomalous traffic events, A low-cost approach. New Security Paradigms Workshop.

Timofte, J., 2008. Intrusion detection using open source tools. Informatica Economica J., 2: 75-79.
Direct Link  |  

Tran, D., W. Ma and D. Sharma, 2008. Automated feature weighting for network anomaly detection. Int. J. Comput. Sci. Network Security, 8: 173-178.

Vigna, G., S. Eckmann and R. Kemmerer, 2000. Attack languages. Proceedings of the IEEE Information Survivability Workshop, (ISW`00), IEEE Computer Society Press, Boston, MA, USA., pp: 163-166.

Vokorokos, L., A. Balaz and M. Chovanec, 2006. Intrusion detection system using self organizing map. Acta Electrotechnica Informatica, 6: 1-6.
Direct Link  |  

Wang, H., D. Xhang and K.G. Shin, 2004. Change-point monitoring for the detection of DOS attacks. IEEE Trans. Dependable Secure Comput., 1: 193-208.
CrossRef  |  

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved