Trustworthy Data Analysis and Sensor Data Protection in Cyber-Physical Systems

Ulybyshev, Denis; Yılmaz, İbrahim; Northern, Bradley; Kholodilo, Vadim; Rogers, Michael J.

doi:10.1145/3445969.3450432

Cited by 8 publications

(11 citation statements)

References 18 publications

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“…There are various application areas where efforts are being made to address security and privacy issues in CPSs. In the field of Industrial Control Systems, studies have focused on addressing data theft and intrusion attacks [12,13]. Solutions such as secure data containers and Intrusion Detection Systems (IDS) have been proposed to safeguard data integrity and monitor system behavior.…”

Section: Security and Privacymentioning

confidence: 99%

“…Data Breaches [10,12,30] Unauthorized data access Unauthorized access to sensitive data stored in CPS systems.…”

Section: Software-based Threatsmentioning

confidence: 99%

“…Bayesian filtering [24] Machine Learning Robotic Process Automation [39] Regression Neural network Regression [12,15,18] Linear Regression [18] Clustering Unsupervised Learning…”

Section: Artificial Intelligencementioning

confidence: 99%

“…Deep Learning [15,27,42] Supervised Learning K-Means Clustering [41] Gaussian Mixture [42] Support Vector Machine [12,15,18,21,40] Classification Naive Bayes [21,24] Decision Tree [18,21] Random Forest [12,13,21] K-Nearest Neighbors [12,15,18,21] AI is a computer science discipline that enables machines to think and behave like humans using methods such as machine learning, deep learning, game theory, optimization theory, and evolutionary algorithms. Bayesian filtering [24] and robotic automation [39] are being used to protect sensitive data CPSs.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…addressing security and privacy issues in CPS, several AI techniques are employed, with supervised machine learning algorithms being the most prevalent, particularly classification algorithm [12,13,15,18,21,24,40]. The classification ML algorithms can be used to train models that can classify data as normal or anomalous.…”

Section: Artificial Intelligencementioning

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%

“…Data Breaches [10,12,30] Unauthorized data access Unauthorized access to sensitive data stored in CPS systems.…”

Section: Software-based Threatsmentioning

confidence: 99%

“…Bayesian filtering [24] Machine Learning Robotic Process Automation [39] Regression Neural network Regression [12,15,18] Linear Regression [18] Clustering Unsupervised Learning…”

Section: Artificial Intelligencementioning

confidence: 99%

Section: Artificial Intelligencementioning

confidence: 99%

Section: Artificial Intelligencementioning

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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An analytical survey of cyber‐physical systems in water treatment and distribution: Security challenges, intrusion detection, and future directions

Gulzar,

Mustafa

2024

Security and Privacy

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Since the inception of the Industrial 4.0 revolution, industrial cyber‐physical systems (CPSs) have become integral to critical infrastructures and industrial sectors, including water treatment and distribution systems. Integrating physical and digital worlds has made communication systems within these plants—comprising actuators, sensors, and controllers—vulnerable to advanced cyber‐attacks. Safeguarding the nation's critical infrastructure has thus attracted significant interest from both academia and industry. This article thoroughly examines water treatment and distribution CPSs, detailing their architectural design, devices, applications, and security standards. It analyzes various cyber‐attacks and explores CPS security vulnerabilities and their detection and mitigation techniques. Additionally, it reviews the trends in machine learning (ML) and deep learning (DL) intrusion detection system (IDS) solutions, highlighting their advantages and disadvantages. The article evaluates current datasets and testbeds, identifying some of the best‐performing IDS algorithms tested on each dataset compared to previous research, which could serve as benchmarks in this field. Finally, it proposes data augmentation techniques to generate comprehensive datasets, identifies research gaps, and suggests potential improvements to enhance IDS performance.

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SEMAFORE: Secure Mobile Field Diagnostics for Cyber-Physical Systems

Seyler

Ulybyshev

2023

Lecture Notes in Networks and Systems

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Trustworthy Data Analysis and Sensor Data Protection in Cyber-Physical Systems

Cited by 8 publications

References 18 publications

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

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

An analytical survey of cyber‐physical systems in water treatment and distribution: Security challenges, intrusion detection, and future directions

SEMAFORE: Secure Mobile Field Diagnostics for Cyber-Physical Systems

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