Dimensionality Reduction and Visualization of Network Intrusion Detection Data

Nowadays, network intrusion detection is researched extensively due to increasing global network threats. Many researchers propose to incorporate machine learning techniques in network intrusion detection systems since these techniques allow for automated intrusion detection with high accuracy. Furthermore, dimensionality reduction techniques can improve the performance of machine learning models, and as such, are widely used as a pre-processing step. Nevertheless, many researchers consider machine learning techniques as a black box because of its complex intrinsic mechanism. Visualization plays an important role in facilitating the understanding of such sophisticated techniques because visualization is able to offer intuitive meaning to the machine learning results. This research investigates the performance of two dimensionality reduction techniques on network intrusion detection datasets. In addition, this work also demonstrates visualizing the resulting data in 3-dimensional space. The purpose of this is to possibly gain insight into the results, which can potentially aid in the improvement of machine learning performance.