Particle Swarm Optimization For Adaptive Anomaly-Based Intrusion Detection System Using Fuzzy Controller
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - October Issue 2013 by IJCTT Journal | ||
| Volume-4 Issue-10 | ||
| Year of Publication : 2013 | ||
| Authors :K.Kavitha , S.Ranjitha Kumari |
K.Kavitha , S.Ranjitha Kumari"Particle Swarm Optimization For Adaptive Anomaly-Based Intrusion Detection System Using Fuzzy Controller"International Journal of Computer Trends and Technology (IJCTT),V4(10):3536-3541 October Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract:- The major work of intrusion detection systems is used to detect the anomaly and new attackers in the networks, even still various false alarms are caused in order to neglect this necessary feature. Existing system present an anomaly-based intrusion detection system to improve the system performance. Fuzzy rule-based modeling and fuzzy controller are used to create a detection model in the training phase and update this model in the test phase respectively. After that, system user verifies these decisions and fuzzy controller tunes detection model using system user’s feedbacks. To improve the accuracy of detect the anomaly in the system. The proposed system is mainly concentrate on finding the optimum membership functions of a fuzzy system using particle swarm optimization (PSO) algorithm. The proposed algorithm it is used to optimize the Gaussian membership functions of the fuzzy model system. It is clearly proved that the optimized membership functions (MFs) provided better performance than a fuzzy model for the same system, when the MFs were heuristically defined.PSO has no evolution operators such as crossover and mutation.
References -
[1] T. Bhaskar, N. Kamath and S.D. Moitra, “A hybrid model for network security systems: Integrating intrusion detection system with survivability,” International Journal of Network Security, vol. 7, no. 2, pp. 249–260, 2008.
[2] S. M. Bridges and R. B.Vaughn, “Fuzzy data mining and genetic algorithms applied to intrusion detection,” Proceedings of the National Information Systems Security Conference, Baltimore, MD, pp. 16-19, 2000.
[3] John E. Dickerson and Julie A. Dickerson, “Fuzzy network profiling for intrusion detection,” Proceedings of NAFIPS 19th International Conference of the North American Fuzzy Information Processing Society, pp. 301–306, Atlanta, USA, July 2000.
[4] S. S. Kandeeban and R. S. Rajesh, “Integrated intrusion detection system using soft computing,” International Journal of Network Security, vol. 10, no. 2, pp. 87-92, 2010.
[5] Y. Liao, V. R. Vemuri and A. Pasos, “Adaptive anomaly detection with evolving connectionist systems,” Journal of Network and Computer Applications, vol. 30, pp. 60–80, 2007.
[6] M. V. Mahoney, P. K. Chan, “Learning non-stationary models of normal network traffic for detecting novel attacks,” Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada, pp. 376–385, 2002.
[7] Rasoulifard, A. Ghaemi, and M. Kahani, “Incremental hybrid intrusion detection using ensemble of weak classifiers,” Advances in Computer Science and Engineering: 13th International CSI, Iran, pp. 577-584, March 2008.
[8] N. Toosi and M. Kahani, “A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers,” Computer Communications, vol. 30, pp. 2201-2212, 2007.
Keywords :— Adaptive anomaly-based intrusion detection, fuzzy-rule based modeling, fuzzy control, PSO.