Precise Feature Selection and Case Study of Intrusion Detection in an Industrial Control System (ICS) Environment

TL;DR

This paper introduces a matrix-rank based $k$-medoids algorithm and a compensation method for feature selection, validated through intrusion detection in an industrial control system dataset, enhancing accuracy and robustness.

Contribution

It proposes a novel matrix-rank based $k$-medoids algorithm and a compensation method for feature relevance, improving feature selection in multi-class datasets.

Findings

The new $k$-medoids algorithm guarantees all independent medoids.

The compensation method addresses class imbalance in datasets.

Case study confirms improved intrusion detection performance.

Abstract

This paper presents analytical techniques to improve redundancy and relevance assessment for precise selection of features in practical multi-class raw datasets. We propose a matrix-rank based $k$-medoids algorithm that guarantees to output all independent medoids. The new algorithm uses matrix rank as a robust indicator, while a traditional $k$-medoids algorithm depends on specific datasets and how the distance between any of two features is defined. Another advantage is that the total number of operations in the nested loops is bounded, different from some $k$-medoids algorithms that involve random search. Sparse regression is an efficient tool for feature relevance analysis, but its outcome can depend on what labeled datasets are employed. A compensation method is introduced in this paper to handle the unequality of class-occurrence in a practical raw dataset. To assess the proposed techniques quantitatively, an existing Industrial Control System (ICS) dataset is used to perform intrusion detection. The numerical results generated from this case study validate the effectiveness and necessity of the proposed analytical framework.