A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Nicholas Jeffrey, Qing Tan, José R. Villar

Research output: Contribution to journalJournal Articlepeer-review

1 Citation (Scopus)

Abstract

Cyber-Physical Systems (CPS) are integrated systems that combine software and physical components. CPS has experienced rapid growth over the past decade in fields as disparate as telemedicine, smart manufacturing, autonomous vehicles, the Internet of Things, industrial control systems, smart power grids, remote laboratory environments, and many more. With the widespread integration of Cyber-Physical Systems (CPS) in various aspects of contemporary society, the frequency of malicious assaults carried out by adversaries has experienced a substantial surge in recent times. Incidents targeting vital civilian infrastructure, such as electrical power grids and oil pipelines, have become alarmingly common due to the expanded connectivity to the public internet, which significantly expands the vulnerability of CPS. This article presents a comprehensive review of existing literature that examines the latest advancements in anomaly detection techniques for identifying security threats in Cyber-Physical Systems. The primary emphasis is placed on addressing life safety concerns within industrial control networks (ICS). A total of 296 papers are reviewed, with common themes and research gaps identified. This paper makes a novel contribution by identifying the key challenges that remain in the field, which include resource constraints, a lack of standardized communication protocols, extreme heterogeneity that hampers industry consensus, and different information security priorities between Operational Technology (OT) and Information Technology (IT) networks. Potential solutions and/or opportunities for further research are identified to address these selected challenges.

Original languageEnglish
Article number3283
JournalElectronics (Switzerland)
Volume12
Issue number15
DOIs
Publication statusPublished - Aug. 2023

Keywords

  • AI/ML in CPS
  • anomaly detection in {CPS, IoT, IIoT, SCADA}
  • machine learning in IoT security
  • security threats to {CPS, IoT, IIOT, SCADA}

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