Study of Support Set Generation Techniques in LAD for Intrusion Detection

Support Set generation is an essential process in the Logical Analysis of Data (LAD). The process of binarization results in an increase in the dimensions of the dataset, which can make the classification process more challenging. The support set generation step is performed to select the important features from the binarized dataset. In this paper, five techniques, namely Set covering problem, Mutual Information Greedy algorithm, Information Gain, Gain ratio, and Gini Index, are used to find the minimal support set for the classification of the Intrusion Detection dataset. LAD uses partially defined Boolean functions to generate positive and negative patterns from the historical observations, which are then transformed into rules for the classification of future observations. The LAD classifier is built using different techniques, and their performances on the NSL-KDD dataset are recorded.