A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

Oyeniyi Akeem Alimi, Khmaies Ouahada, Adnan M. Abu-Mahfouz, Suvendi Rimer and Kuburat Oyeranti Adefemi Alimi
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Oyeniyi Akeem Alimi: Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
Khmaies Ouahada: Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
Adnan M. Abu-Mahfouz: Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
Suvendi Rimer: Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
Kuburat Oyeranti Adefemi Alimi: Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa

Sustainability, 2021, vol. 13, issue 17, 1-19

Abstract: Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.

Keywords: artificial neural network; classification; critical infrastructures; industrial control systems; intrusion detection; supervised learning; SCADA; support vector machine (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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