Adaptive Anomaly-Based Intrusion Detection System Using Fuzzy Controller

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. Keywords— Adaptive anomaly-based intrusion detection, fuzzyrule based modeling, fuzzy control, PSO.