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Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Journal of Statistics and Management Systems
Open Access Research Article

An intrusion detection system for industrial IoT using chi-square feature selection

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pp. 1021–1031Vol. 27Issue 5July 2024DOI: 10.47974/JSMS-1303XML
Received:
14 May 2024
Published Online:
05 Aug 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1303
Pages:
1021–1031

Abstract

The expansion of the Industrial Internet of Things (IIoT) has led to advancements in various industries, but that has also exposed infrastructure which is critical to increasing cyber threats. In response, this paper addresses the important role of feature selection in improving the performance of Intrusion Detection Systems (IDS). Utilizing the Edge-IIoTset, this paper introduces a model which includes data pre-processing, feature selection using chi-square method, intrusion detection using machine learning and performance analysis. The experimentation reveals that the initial implementation of the PART model, using all features, yields accuracy of 97.5162% but shows a high model build-up time of 61.23 seconds. Further refinement through the chi-square feature selection method identifies a subset of the top ranked 20 features based on chi-sqauare score, achieving an enhanced accuracy of 97.5202% with a significantly reduced model build-up time of 21.54 seconds. Comparative analysis with other feature selection methods establishes the superiority of the chi-square method for the Edge-IIoT dataset.

Keywords

Industrial Internet of ThingsCybersecurityIntrusion detection systemFeature selectionMachine learning

Subject Classifications

68T99

References

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