Implementation of Network Intrusion Detection System Using Soft Computing Algorithms (Self Organizing Feature Map and Genetic Algorithm)

Joël T. Hounsou, Thierry Nsabimana, Jules Degila
2019 Journal of Information Security  
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
more » ... roaches 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. process performed in nature, i.e. crossover and mutation [6] . GA is chosen because of some of its nice properties, e.g., robust to noise, no Problem Statement Most public or/and private companies, Banks, Institutions and Universities defeat against network attacks by applying varied traditional or advanced security methods namely firewalls, Antivirus and intrusion detection systems. The IT Managers of the companies, Banks, Institutions and Universities suffer from the lack of efficient security methods to secure multiple departments of their Companies, Banks and Universities before computer network attack. It is a challenging security issues for IT Managers of the companies to protect their data and network systems from attacks with rapidly growing volume of network traffic and the number of attacks. Multiple commercials and open source Network Intrusion Detection Systems are currently used in the companies, banks and so far to protect their information and local network systems from attacks. Unfortunately, the Intrusions Detection Systems (IDSs) suffer from high false positive rate generated by Anomaly detection method which leads to very poor accuracy J. T. Hounsou et al. of anomaly detection system and high false negative rate generated by Misuse or Signature detection method due to the inability of detecting unknown attacks. J. T. Hounsou et al.
doi:10.4236/jis.2019.101001 fatcat:fwaa76rvtnbibj4o44nxjx3gvy