Effect of Genetic Algorithm on Artificial Neural Network for Intrusion Detection System - Dastanpour, Amin; Ibrahim, Suhaimi; Mashinchi, Reza (International Journal of Computer Sciences and Engineering, 2025)
By increasing the advantages of network based systems and dependency of daily life with them, the efficient operation of network based systems is an essential issue. Since the number of attacks has significantly increased, intrusion detection systems of anomaly network behavior have increasingly attracted attention among research community. Intrusion detection systems have some capabilities such as adaptation, fault tolerance, high computational speed, and error resilience in the face of noisy information. So, construction of efficient intrusion detection model is highly required for increasing the detection rate as well as decreasing the false detection. . This paper investigates applying the following methods to detect the attacks intrusion detection system and understand the effective of GA on the ANN result: artificial Neural Network (ANN) for recognition and used Genetic Algorithm (GA) for optimization of ANN result. We use KDD CPU 99 dataset to obtain the results; witch shows the ANN result before the efficiency of GA and compare the result of ANN with GA optimization.