Improved Intrusion Detection Accuracy Based on Optimization Fast Learning Network Model

In current days, accuracy of Intrusion detection system still represents one of main limitation of the system. Moreover, hybrid model based on intrusion detection system achieved better results in compared with model based single algorithm. This work presents a new intrusion detection system based on hybrid model based on Fast Learning Network (FLN) and Genetic Algorithm (GA) which called (GA-FLN), NSL-KDD dataset used to evaluate the proposed model. The results of new model represent with different numbers of hidden neurons to analysis the impact of neurons based on accuracy of intrusion detection system.