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Enhancing Intrusion Detection using CRF and Layered Approach for Discussion Forum Web Application

Ketaki Mohan Patil, A.B. Chougule


Intrusion detection systems provide way of detecting attacks on systems by monitoring network activities for malicious or abnormal behaviors. As the use of network has become a part of each and everyone's daily routine today's intrusion detection system faces number of challenges. Network intrusion detection system has become an important component in network security. In this paper we address two issues one Conditional Random Fields (CRFs) and second layered approach. We integrate both of them to improve overall accuracy and efficiency. The system will detect attacks like Probe layer attack, Dos attack, R2L attack and U2R attack.


Intrusion Detection, Conditional Random Fields, Layered Approach, Network Security

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Kapin Kumar Gupta, Bai Kunth Nath, Senior member IEEE and Ramamohanraw Kotagiri, member IEEE " Layered Approach using Conditional Random Fields for Intrusion Detection ".IEEE Transactions on Dependable and Secure Computing, VOL. 7 No1. Jan-March 2010.

Srinoy S Kurutach W, Chimphlee, "Network anomaly detection using soft computing" proceedings of world academy of science, engineering and technology, VOL9 pp140-144,2005.

Charles Sutton, "An introduction to Conditional Random Fields", University of Edinburgh, 17November2010.

Veeraju Gampala,Shrilakshmi Inugati, Satish Muppidi, "Intrusion detection using pipelining of layers with Conditional Random Fields in multicore processors", International journal of advanced trends in computer science and engineering, VOL2, No1 pages 01-06 (2013).

S Devaraju, S Ramakrishnan, "Detection of accuracy for intrusion detection system using neural network classifier", International conference on Information Systems and computing (ICISC-2013), India.

Neveen I Ghali, "Feature selection for effective anomaly - based intrusion detection", International journal of computer science and network security VOL9 No3, March 2009.

Wenke Lee et at., “A Data Mining Framework for Building Intrusion Detection Model. In Proceedings of the IEEE Symposium on Security and Privacy, pages 120–132, IEEE, 1999.

Dalila Boughaci, Habiba Drias, Ahmed Bendib, Youcef Bouznit, and Belaid Benhamou.Distributed Intrusion Detection Framework Based on Mobile Agents. In Proceedings of the International Conference on Dependability of Computer Systems, pages 248–255. IEEE 2006.

JaiSundarBalasubramaniyan, Garcia-Fernandez, David Isacoff, Eugene H Spafford and Diego Zamboni. “Architecture for Intrusion Detection Using Autonomous Agents”. Proceeding of the 14th Annual Computer Security Applications Conference, pages 13–24. IEEE, 1998.

Yu-Sung Wu, Bingrui Foo, Yongguo Mei and Saurabh Bagchi. Collaborative Intrusion Detection System (CIDS): A Framework for Accurate and Efficient IDS. In Proceedings of the 19th Annual Computer Security Applications Conference, pages 234–244. IEEE, 2003.

Elvis Tombini, Herve Debar, Ludovic Me, and Mireille Ducasse. A Serial Combination of Anomaly and Misuse IDSes Applied to HTTP Traffic. In Proceedings of the 20th Annual Computer Security Applications Conference, pages 428–437. IEEE, 2004.

Snort, “A Network based Intrusion Detection System.” Last accessed, November 30,2008.

Pavel Laskov," Intrusion Detection and Malware Analysis Signature-based IDS",Wilhelm Schickard Institute for Computer Science.

De Ocampo, Frances Bernadette C, Del Castillo, Trisha Mari L.,"Automated Signature Creator for a Signature Based Intrusion Detection System with Network Attack" International Journal of Cyber-Security and Digital Forensics (IJCSDF) 2(1): The Society of Digital Information and Wireless Communications, 2013 (ISSN: 2305-0012).

Eric Newcomer and Greg Lomow. Understanding SOA with Web Services. Addison-Wesley Professional, 2004.


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