Intrusion Detection using Ensemble Learning on Combined Features

Network intrusions may illicitly retrieve data/information, or prevent legitimate access. Reliable detection of network intrusions is an important problem, misclassification of an intrusion is an issue by the resultant overall reduction of accuracy of detection. A variety of potential methods exist to develop an improved system to perform classification more accurately. Feature selection is one area that may be utilized to successfully improve performance by initially identifying sets and subsets of features that are relevant and non-redundant. Within this paper explicit pairings of features have been investigated in order to determine if the presence of pairings has a positive effect on classification, potentially increasing the accuracy of detecting intrusions correctly. In particular, classification using the ensemble algorithm, StackingC, with F-Measure performance and derived Information Gain Ratio, as well as their subsequent correlation as a combined measure, are presented.