Ensemble learning utilising feature pairings for intrusion detection

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 in and of itself reducing overall accuracy of detection. A variety of potential methods exist to develop an improved system to perform classification more accurately. Feature selection is one potential area that may be utilized to successfully improve performance by initially identifying sets and subsets of features that are relevant and nonredundant. 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, is presented.