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, Kuburat Oyeranti Adefemi Alimi

Research output: Contribution to journalReview articlepeer-review

33 Citations (Scopus)

Abstract

Supervisory Control and Data Acquisition (SCADA) systems play a significant role in pro-viding 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.

Original languageEnglish
Article number9597
JournalSustainability
Volume13
Issue number17
DOIs
Publication statusPublished - 1 Sept 2021

Keywords

  • Artificial neural network
  • Classification
  • Critical infrastructures
  • Industrial control systems
  • Intrusion detection
  • SCADA
  • Supervised learning
  • Support vector machine

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Computer Networks and Communications
  • Management, Monitoring, Policy and Law

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