A common practice in modern computer networks is the deployment of Intrusion Prevention Systems (IPSs) for the purpose of identifying security threats. Such systems provide alerts on suspicious activities based on a predefined set of rules. These alerts almost always contain high percentages of false positives and false negatives, which may impede the efficacy of their use. Therefore, with the presence of high numbers of false positives and false negatives, the analysis of network traffic data can be ineffective for decision makers which normally require concise, and preferably, visual forms to base their decisions upon. Machine learning techniques can help extract useful information from large datasets. Combined with visualisation, classification could provide a solution to false alerts and text-based outputs of IPSs.
This research developed two new classification techniques that outperformed the traditional classification methods in accurate classification of computer network traffic captured by an IPS framework. They are also highly effective. The main purpose of these techniques was the effective identification of malicious network traffic and this was demonstrated via extensive experimental evaluation (where many experiments were conducted and results are reported in this thesis). In addition, an enhancement of the principal component analysis (PCA) was presented as part of this study. This enhancement proved to outperform the classical PCA on classification of IPS data.
Details of the evaluation and experiments are provided in this thesis. One of the classification methods described in this thesis achieved accuracy values of 98.51% and 99.76% on two computer network traffic dataset settings, whereas the Class-balanced Similarity Based Instance Transfer Learning (CB-SBIT) algorithm achieves accuracy values of 93.56% and 96.25% respectively on the same dataset settings. This means the proposed method outperforms the state-of-the-art algorithm.
As for the PCA enhancement mentioned above, using its resulting principal components as inputs to classifiers leads to improved accuracy when compared to the classical PCA
