Abstract
M.Phil. (Electrical and Electronic Engineering)
Network security has become increasingly important as more and more applica-
tions are making their way into the market. The research community has proposed
various methods to build a reliable network intrusion detection system to detect
unauthorised activities in networked systems. However many network intrusion
detection systems that have been reported in literature su er from an excessive
number of false positives, false negatives, and are unable to cope with new, elegant
and structured attacks. This is mainly because most network intrusion detection
systems rely on security experts to analyze the network tra c data and manually
construct intrusion detection rules.
This study proposes to use a machine learning technique such as neural network
approach to anomaly based network intrusion detection system (NIDS). The main
objective for this study is to construct an NIDS model that will produce approx-
imate to zero false positive or no false positive at all and have high degree of
accuracy in detecting network attacks. The neural network (NN) model is trained
on a biometric networked system dataset simulated in the study, containing strictly
replayed and normal network tra c that encourage the development of the pro-
posed NIDS.
By analyzing the NN{based NIDS results, the study reached the false positive rate
of 0, and high accuracy rate of 100 percent. To support the results obtained in
this study, the performance of the NN{based NIDS was compared to two other
classi cation methods (k{nearest neighbor algorithm (KNN) and Naive Bayes).
The results obtained from KNN and naive Bayes were 99.87 and 99.75 percent
respectively. These results show that the proposed model can successfully be used
as an e ective tool for solving complicated classi cation problems such as NIDS.