The week's pick
Reseach Article
Network Intrusion Detection using Layered Approach and Hidden Markov Model
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 93 - Number 13 |
| Year of Publication: 2014 |
| Authors: Archana I. Patil, Girish Kumar Patnaik, Ashish T. Bhole |
10.5120/16278-6049
|
Archana I. Patil, Girish Kumar Patnaik, Ashish T. Bhole . Network Intrusion Detection using Layered Approach and Hidden Markov Model. International Journal of Computer Applications. 93, 13 ( May 2014), 38-43. DOI=10.5120/16278-6049
Abstract
Traditional intrusion detection systems uses either anomaly based or signature based technique. Both of these techniques have some problems. In anomaly based intrusion detection, the strategy is to suspect an unusual activity and thereby to continue further investigation. This approach is particularly effective against novel attacks. Signature based intrusion detection system detects known attacks timely and efficiently. For this approach, it is important to know the attack. The proposed system introduces a hybrid of anomaly based and signature based technique. The proposed system uses layered approach to get the results faster. Each layer in the layered approach is independent to detect and block an attack. Four different layers Probe, U2R, R2L and DOS are assigned with different features. The proposed hybrid technique with Hidden Markov Model can give better results compared to signature based and anomaly based intrusion detection techniques alone.
References
- Kapil Kumar Gupta, Baikunth Nath, "Layered Approach Using Conditional Random Fields for Intrusion Detection", IEEE Transaction On Dependable and Secure Computing, Vol. 7, No. 1, January –March 2010.
- T. Abraham, "IDDM: Intrusion Detection Using Data Mining Techniques", International Journal of Network Security & Its Applications (IJNSA), Vol. 2, No. 2, 2008.
- N. B. Amor, S. Benferhat, and Z. Elouedi, "Naive Bayes vs. Decision Trees in Intrusion Detection Systems", Proc. ACM Symp. Applied Computing, 2004.
- Gupta, Kapil Kumar, Baikunth Nath, and Kotagiri Ramamohanarao, "Conditional random fields for intrusion detection", In Advanced Information Networking and Applications Workshops, 2007, AINAW'07. 21st IEEE International Conference on, vol. 1, pp. 203-208, 2007.
- Yusufovna, Sattarova Feruza, "Integrating intrusion detection system and data mining", In Ubiquitous Multimedia Computing, 2008. UMC'08. IEEE International Symposium on, pp. 256-259, 2008.
- Christopher Kruegel, Darren Mutz, William Robertson, Fredrik Valeu, "Bayesian Event Classi?cation for Intrusion Detection", In Computer Security Applications Conference, 2003. Proceedings. 19th IEEE Annual, pp. 14-23, 2003.
- Portnoy, Leonid, "Intrusion detection with unlabeled data using clustering" 2000.
- Wu, Yu-Sung, Bingrui Foo, Yongguo Mei, and Saurabh Bagchi, "Collaborative intrusion detection system (CIDS): a framework for accurate and efficient IDS", In Computer Security Applications Conference, 2003. Proceedings. 19th IEEE Annual, pp. 234-244, 2003.
- Autonomous Agents for Intrusion Detection, 2010. http://www. cerias. purdue. edu/research/aafid/
- Probabilistic Agent Based Intrusion Detection, 2010. http://www. cse. sc. edu/research/isl/agentIDS. shtml
- Akbar, Shaik, K. Nageswara Rao, and J. A. Chandulal, "Intrusion detection system methodologies based on data analysis", International Journal of Computer Applications, Vol. 5, No. 2, 2010.
- Wang, Wei, Xiaohong Guan, Xiangliang Zhang, and Liwei Yang, "Profiling program behavior for anomaly intrusion detection based on the transition and frequency property of computer audit data", computers & security, Vol. 25, No. 7, 2006.
- Landwehr, Carl E. , Alan R. Bull, John P. McDermott, and William S. Choi, "A taxonomy of computer program security flaws", ACM Computing Surveys (CSUR), Vol. 26, No. 3, 1994.
- Nicholas Pappas, "Network IDS & IPS Deployment Strategies", 2008.
- Bouzida, Yacine, and Sylvain Gombault, "Eigenconnections to intrusion detection", In Security and Protection in Information Processing Systems, pp. 241-258. Springer US, 2004.
- Kim, Dong Seong, and Jong Sou Park, "Network-based intrusion detection with support vector machines" , In Information Networking, pp. 747-756. Springer Berlin Heidelberg, 2003.
Index Terms
Keywords