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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:10528 |
| Date | 09 April 2014 |
| Creators | Mgabile, Tinny |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
| Rights | University of Johannesburg |
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