Author(s)

Joël T. Hounsou, Thierry Nsabimana, Jules Degila

Institut de Mathématiques et de Sciences Physiques, Porto-Novo, Benin, Université d’Abomey Calavi.

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.

SOFM, Intrusion Detection Systems, False Positive Rate, Detection Rate, KDD Cup 99, GA

Hounsou, J. , Nsabimana, T. and Degila, J. (2019) Implementation of Network Intrusion Detection System Using Soft Computing Algorithms (Self Organizing Feature Map and Genetic Algorithm). Journal of Information Security, 10, 1-24. doi: 10.4236/jis.2019.101001.