A Machine Learning approach to Intrusion Detection in Water Distribution Systems – A Review

Mboweni, Ignitious V.; Abu‐Mahfouz, Adnan M.; Ramotsoela, Daniel

doi:10.1109/iecon48115.2021.9589237

Cited by 5 publications

(7 citation statements)

References 24 publications

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“…Machine learning algorithms have been applied to detect data breaches, improve cloud security, and develop frameworks for IoT cloud deployment. The significance of addressing security breaches in Healthcare Cyber-Physical Systems (HCPS) is emphasized to prevent unauthorized access to sensitive health data and potential misdiagnosis or incorrect treatment [10,16,17].…”

Section: Security and Privacymentioning

confidence: 99%

“…Water treatment is another major application area where security attacks have occurred in CPSs. Researchers have focused on intrusion detection mechanisms [18][19][20], false data injection [18], DoS attacks [21], spoofing [21,22], authentication [23], and the identification of anomalies in water systems [17,19,24]. Machine learning methods have been utilized for anomaly detection and vessel trajectory prediction to improve maritime surveillance [25].…”

Section: Security and Privacymentioning

confidence: 99%

“…Anomaly Detection [17][18][19]24] Behavior anomaly detection Detecting deviations from expected behavior or patterns within CPS systems that may indicate potential security breaches or system malfunctions.…”

Section: Loss Of Customer Trustmentioning

confidence: 99%

“…Malicious Attacks [17,21] Tampering with system components Unauthorized alteration, modification, or sabotage of hardware or software components within CPS systems.…”

Section: Loss Of System Availabilitymentioning

confidence: 99%

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A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems

Bandi¹

2023

Preprint

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Background:As cyber-physical systems (CPS) continue to grow, it is progressively crucial to address the challenges of data protection using artificial intelligence (AI). Objective:The goal of this research is to provide an up-to-date overview of the security and privacy issues of CPS that incorporate AI techniques. Method:To achieve this, the author conducted a systematic literature review, focusing on 35 relevant articles. Results:The data collected from these studies was then categorized into three main areas: 1) different security and privacy issues, 2) application areas and vulnerabilities, and 3) the AI techniques used to address security and privacy concerns. The literature review highlights that intrusion detection and cyberattacks are the most commonly studied areas in CPS, while Machine Learnning (ML)-based attacks and vessel trajectory are less explored. The review identifies various CPS applications such as water treatment, energy, healthcare, and transportation that address security and privacy concerns. However, a relatively small proportion of studies focused on the manufacturing domain. The review also notes that while supervised machine learning algorithms under the classification category are commonly used to address data protection issues, there are comparatively fewer studies that have implemented automation processes using robots and deep learning. Limitations:The articles related to blockchain-based research were not included in this review to focus solely on AI techniques. Conclusion:The results of this analysis indicate that there is a significant need for innovative AI/ML techniques to protect intelligent systems and networks from ML-based security threats.

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Section: Security and Privacymentioning

confidence: 99%

Section: Security and Privacymentioning

confidence: 99%

Section: Loss Of Customer Trustmentioning

confidence: 99%

“…Malicious Attacks [17,21] Tampering with system components Unauthorized alteration, modification, or sabotage of hardware or software components within CPS systems.…”

Section: Loss Of System Availabilitymentioning

confidence: 99%

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A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems

Bandi¹

2023

Preprint

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“…ML algorithms have been applied to detect data breaches, improve cloud security, and develop frameworks for IoT cloud deployment. The significance of addressing security breaches in healthcare cyber-physical systems (HCPS) is emphasized to prevent unauthorized access to sensitive health data and potential misdiagnosis or incorrect treatment [10,16,17].…”

Section: Introductionmentioning

confidence: 99%

A Taxonomy of AI Techniques for Security and Privacy in Cyber–Physical Systems

Bandi

2024

JCCE

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This research paper addresses the concerns related to security and privacy in cyber-physical systems (CPS) and explores the role of artificial intelligence (AI) in addressing these concerns. This paper presents a comprehensive classification of various security and privacy threats in CPS, providing an organized overview of potential risks, economic loss, and enabling effective risk assessment. This paper highlights how AI can help address the security and privacy concerns in CPS by presenting a detailed flow chart that illustrates the stepby-step process of using AI and machine learning (ML) techniques to detect security and privacy issues. This integrated approach serves as a guide for designing ML-based secure CPS, enabling proactive defense mechanisms and improving incident response and recovery. Furthermore, the research explores the various AI techniques that can be employed to address security and privacy concerns in CPS. A taxonomy of ML techniques specifically relevant to security and privacy issues is provided, offering insights into the potential applications of these techniques. In conclusion, this research emphasizes the significance of addressing security and privacy concerns in CPS and highlights the role of AI in tackling these challenges.

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