Commenced in January 2007
Paper Count: 31198
Intrusion Detection Using a New Particle Swarm Method and Support Vector Machines
Authors: Essam Al Daoud
Abstract:Intrusion detection is a mechanism used to protect a system and analyse and predict the behaviours of system users. An ideal intrusion detection system is hard to achieve due to nonlinearity, and irrelevant or redundant features. This study introduces a new anomaly-based intrusion detection model. The suggested model is based on particle swarm optimisation and nonlinear, multi-class and multi-kernel support vector machines. Particle swarm optimisation is used for feature selection by applying a new formula to update the position and the velocity of a particle; the support vector machine is used as a classifier. The proposed model is tested and compared with the other methods using the KDD CUP 1999 dataset. The results indicate that this new method achieves better accuracy rates than previous methods.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080708Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1659
 H. Zhang, X. Wang, Y Wang, "Network Connection Based Intrusion detection Using Rough Set Classification," Proceedings 2006 International Conference on Communications, Circuits and Systems 3: 2128 2132, 2006.
 A.O. Adetunmbi, B.K. Alese, O.S. Ogundele, S.O. Falaki, A "Data Mining Approach to Network Intrusion Detection," Journal of Computer Science & Its Applications, 14 (2): 24 -37, 2007.
 D. M. Farid, N. Harbi, M. Z. Rahman, Combining Nave Bayes and Decision Tree for Adaptive Intrusion Detection, International Journal of Network Security & Its Applications, 2(2):12-25, 2010.
 D. M. Farid, N. Harbi, M. Z. Rahman, Anomaly Network Intrusion Detection Based on Improved Self Adaptive Bayesian Algorithm, Journal of Computers, Academy Publisher, 5(1):, 23- 31, 2010.
 A.W. Mohemmed, N.C. Sahoo, T.K. Geok, A new particle swarm optimization based algorithm for solving shortest-paths tree problems, inIEEE CEC 2007, pp. 32213225, 2007.
 M. Clerc, J. Kennedy, "The particle swarm explosion, stability, and convergence in a multidimensional complex space". IEEE Transactions on Evolutionary Computation 6(1): 58-73, 2002.
 D. P. Rini, S. M. Shamsuddin, S. S. Yuhaniz, " Particle Swarm Optimization: Technique, System and Challenges," international Journal of Computer Applications, 14(1): 0975 8887, 2011.
 R.Mendes, J.Kennedy, J. Neves," The fully informed particle swarm: Simpler, maybe better," IEEE Transactions on Evolutionary Computation 8(3) 204 210, 2004.
 R. Parimala, R. Nallaswamy, "Feature selection using a novel particle swarm optimization and its variants," I.J. Information Technology and Computer Science, 5: 16-24, 2012.
 L. Y. Chuang, S. W. Tsai, C.H. Yang, "Catfish Binary Particle Swarm Optimization for Feature Selection" International Conference on Machine Learning and Computing, IPCSIT, IACSIT Press, Singapore, 3: 40-44, 2011.
 B. Pfahringer, "Winning the KDD99 Classification Cup: Bagged Boosting," SIGKDD Explorations, 1: 6566, 2000.
 T. Ambwani, "Multi class support vector machine implementation to intrusion detection," in Proc. of IJCNN: 2300-2305. 2003.
 R. Agarwal, M. V. Joshi, "PNrule: A New Framework for Learning Classifier Models in Data Mining," In Proceedings of First SIAM Conference on Data Mining, Chicago, April 2001. Expanded version available as IBM Research Division Report, RC 21719, April 2000.
 K. K. Gupta, B. Nath, and R. Kotagiri, "Layered Approach using Conditional Random Fields for Intrusion Detection," IEEE Transactionson Dependable and Secure Computing, vol. 5, 2008.
 KDDCUP99: http://kdd.ics.uci.edu/databases/kddcup99/