Hounsou, Joël T.Nsabimana, ThierryDegila, Jules2022-04-212022-04-212018-12-04Hounsou, J.T., Nsabimana, T. and Degila, J. (2019) Im plementation of Network Intrusion Detec tion System Using Soft Computing Algo rithms (Self Organizing Feature Map and Genetic Algorithm). Journal of Information Security, 10, 1-24. https://doi.org/10.4236/jis.2019.1010012153-1242http://hdl.handle.net/123456789/1411In computer security field, an attacker or a hacker can be understood as an unauthorized user attempts to penetrate the security defenses and gain access to the network system in order to violate the integrity, confidentiality and availability of resources. The hackers constantly invent new attacks and disseminate them over the internet [1]. Securing network is a delicate step to protect a company from the most common risks emanating from the Internet as well as from its own local network system. To prevent attacks or to reduce their severity, many solutions exist but no one can be considered satisfactory and complete [2].In today’s world, computer network is evolving very rapidly. Most public or/and private companies set up their own local networks system for the purpose of promoting communication and data sharing within the companies. Unfortunately, their data and local networks system are under risks. With the advanced computer networks, the unauthorized users attempt to access their local networks system so as to compromise the integrity, confidentiality and availability of resources. Multiple methods and approaches have to be applied to protect their data and local networks system against malicious attacks. The main aim of our paper is to provide an intrusion detection system based on soft computing algorithms such as Self Organizing Feature Map Artificial Neural Network and Genetic Algorithm to network intrusion detection system. KDD Cup 99 and 1998 DARPA dataset were employed for training and testing the intrusion detection rules. However, GA’s traditional Fitness Function was improved in order to evaluate the efficiency and effectiveness of the algorithm in classifying network attacks from KDD Cup 99 and 1998 DARPA dataset. SOFM ANN and GA training parameters were discussed and implemented for performance evaluation. The experimental results demonstrated that SOFM ANN achieved better performance than GA, where in SOFM ANN high attack detection rate is 99.98%, 99.89%, 100%, 100%, 100% and low false positive rate is 0.01%, 0.1%, 0%, 0%, 0% for DoS, R2L, Probe, U2R attacks, and Normal traffic respectively.enSOFMIntrusion Detection SystemsFalse Positive RateDetection RateKDD Cup 99GAArticle