Aug 2026· Italian National Conference on Sensors· Vol 26, pp. 5412· 0 citations· 13 references
TL;DR
It is found that traditional risk assessment and testing approaches are insufficient for AI-powered CPS, and a prototype implementation and experimental evaluation are presented along with a case study of protecting a smart manufacturing plant during a ransomware attack using the proposed approach.
Abstract
Cyber-Physical Systems (CPS) are using more AI for smart decisions and automation, but also faces new security issues. This study surveys existing CPS security assessment methodologies across healthcare, automotive, energy, and critical infrastructure, identifying their limitations in addressing emerging digital-physical threats. We find that traditional risk assessment and testing approaches, often network-centric and compliance-driven, are insufficient for AI-powered CPS. New vulnerabilities arise from the tight coupling of cyber and physical components, such as adversarial manipulation of sensors that can cause dangerous misbehavior, supply chain attacks on AI models, and the inability to patch critical devices on the fly. We use AI techniques and problem-solving methods to improve the security of CPS. The framework helps detect threats, monitor system activities, and reduce security risks in real time. It also follows important security and privacy standards such as NIST, IEC 62443, ISO 21434, and GDPR. The system continuously checks CPS operations, uses AI tools to find weaknesses, and supports security compliance. We also study real-world CPS attacks, including industrial malware, car hacking, and medical device attacks, to show the importance of the framework. In this research, we present prototype implementation and experimental evaluation along with a case study of protecting a smart manufacturing plant during a ransomware attack using the proposed approach.
Cyber-physical-human systems (CPHS) integrate human inputs, behaviors, and interfaces for a more effective utilization of artificial intelligence (AI) assisted cyber and physical systems. However, the growing reliance on
Agentic AI
, coupled with the inherent variability of human actions, can introduce unforeseen security challenges. Studies have reported that any security compromise can result in catastrophic failures, safety hazards, and data breaches, disrupting the day-to-day operations of CPHS. Given the scale and complexity of typical CPHS, robust security measures are essential. This paper investigates the security requirements of CPHS encompassing confidentiality, integrity, availability, authentication, and authorization, in the context of CPHS. We also emphasize the role of formal verification methods to establish and guarantee the trustworthiness of agents, which are increasingly integral to these systems. Considering the inherent mutual dependency, we systematically categorize and analyze attack vectors across three dimensions:
data
,
agents
, and
human actors
that can impact the security and trustworthiness of CPHS. Using unmanned aerial vehicles (UAVs) in the defense sector as a prototypical CPHS engineering application, we present a typical mission scenario involving a remotely piloted Medium-Altitude, Long-Endurance (MALE) aircraft designed for Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) to conduct threat assessments. Our comprehensive analysis illustrates how these attack vectors can compromise each dimension, providing actionable insights for security engineers and system architects to design robust security measures in CPHS.
Sandeep K. S. Gupta, Maria Papaioannou, Nicola Dragoni· Human-Intelligent Systems In...· 0 citations
Background: The rapid adoption of Artificial Intelligence (AI)-enabled Cyber-Physical Systems (CPS) has transformed modern healthcare by enabling intelligent patient monitoring, remote diagnosis, wearable health devices, robotic assistance, and real-time clinical decision-making. While these technologies improve healthcare quality and accessibility, their increasing connectivity exposes healthcare infrastructures to a wide range of cyber threats that may compromise patient safety, privacy, and clinical operations.
Methods: This review presents a comprehensive taxonomy of cyber attacks targeting AI-driven healthcare CPS, including attacks on sensors, communication networks, cloud platforms, medical Internet of Things (IoMT) devices, AI algorithms, and control systems. Recent research on intrusion detection, encryption techniques, blockchain, federated learning, explainable AI, trust management, and zero-trust architectures is systematically analyzed to evaluate existing defense mechanisms.
Results: The review identifies major vulnerabilities across healthcare CPS components and summarizes current mitigation strategies designed to improve system resilience, data integrity, patient privacy, and operational reliability. Furthermore, emerging AI-based security solutions capable of detecting sophisticated attacks in real time are critically discussed.
Conclusion: Although significant progress has been achieved in securing healthcare CPS, challenges remain in ensuring scalability, interoperability, regulatory compliance, and trustworthy AI deployment. Future research should focus on developing adaptive, intelligent, and privacy-preserving cybersecurity frameworks capable of supporting next-generation digital healthcare ecosystems.
Dhivya Rathinasamy (Corresponding Author), R. Swathiramya, Vishnu Kumar K et al.· Adolescência e Saúde· 0 citations
A Security-by-Design and risk-based certification framework that combines a six-layer IoT-AI reference architecture with STRIDE-based threat analysis augmented to capture AI-specific threats, including prompt injection and data poisoning is proposed.
Iván Ortiz-Garcés, Roberto O. Andrade· Future Internet· 0 citations
The findings indicate that AI-powered cyber defense significantly enhances threat detection, reduces response time, and improves overall cyber resilience compared to traditional security models, highlighting its critical role in next-generation cybersecurity infrastructures.
Chinedu Eze· International Journal of App...· 0 citations
This review critically analyzes the cybersecurity research published over the past few years on cyber threats across the various layers of the IIoT architecture, publicly available cybersecurity datasets, evaluation practices, and AI-based intrusion detection methods to provide a pathway toward resilient, adaptive, and operationally deployable cybersecurity solutions for next-generation IIoT.
Siddhartha Singhal, Kakelli Anil Kumar· Frontiers in Big Data· 0 citations
This paper investigates a secure-by-design engineering process focusing on the initial architectural design and examines the role that AI-powered agents can play in supporting it, as well as the conditions required for their effective and reliable use.
C. Ponsard, Jean-François Daune· International Conference on...· 0 citations
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