TITLE:
Implementation of Network Intrusion Detection System Using Soft Computing Algorithms (Self Organizing Feature Map and Genetic Algorithm)
AUTHORS:
Joël T. Hounsou, Thierry Nsabimana, Jules Degila
KEYWORDS:
SOFM, Intrusion Detection Systems, False Positive Rate, Detection Rate, KDD Cup 99, GA
JOURNAL NAME:
Journal of Information Security,
Vol.10 No.1,
December
4,
2018
ABSTRACT: 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.