EF2RB: Ensemble of Filter Feature Selection Methods with Ranker Booster for Classification of High Dimensional IoT Intrusion Data

Analysis of high-dimensional Internet of Things (IoT) network data for intrusion detection is an important task, as the data generated by heterogeneous IoT devices is unbalanced, diverse, and context-dependent in nature. Feature Selection (FS) plays a major role in selecting the most informative, highly relevant, and non-redundant features from high-dimensional IoT intrusion datasets. In this paper, we discuss a novel ensemble feature selection method called EF\((^{2})\)RB that incorporates an innovative concept called Ranking with Boosting(RB). The method is evaluated in high-dimensional IoT intrusion datasets using baseline Machine Learning (ML) models and found that the proposed EF\((^{2})\)RB gives a very high detection accuracy on the selected subset of features. Moreover, we tested the proposed EF\((^{2})\)RB method with an IoT intrusion detection system (IDS) and observed that the IDS gives detection accuracy within the range of 95% - 100%. The implementation details of the method are uploaded to the GitHub repository https://github.com/2ez4n0dyX/EF2RB.git.