Proportional Voting based Semi-Unsupervised Machine Learning Intrusion Detection System

Abstract

Feature selection of NSL-KDD data set is usually done by finding co-relationships among features, irrespective of target prediction. We aim to determine the relationship between features and target goals to facilitate different target detection goals regardless of the correlated feature selection. The unbalanced data structure in NSL-KDD data can be relaxed by Proportional Representation (PR). However, adopting PR would deny the notion of winner-take-all by attracting a majority of the vote and also provide a fairly proportional share for any grouping of like-minded data. Furthermore, minorities and majorities would get a fair share of power and representation in data structure distribution. Particle Swarm Optimization (PSO) utilizes attack data for minority while majority employs non-attack data along with targeted classes to increase detection rate and reduce false alarms, especially for R2L and U2R attacks, as the output target goal influences feature selections and corresponding detection rate and false alarm rate. Our simulation study confirms the feasibility of the Voting Representation for minority protection and increased detection rate while reducing false alarms, which is favorable to minority over the majority.