This survey provides a comprehensive, systematic roadmap for achieving cyber-physical system (CPS) resilience by integrating five interconnected themes: system-wide properties, handling data scarcity…

The paper introduces BYOT-CPS, a hybrid cyber-physical testbed that bridges the gap between purely simulated and purely physical IoT testing environments, enabling realistic and scalable security asse…

The paper proposes the Cyber-Physical Data Flow Diagram (CPDFD), a novel modeling technique designed to improve threat identification and risk assessment for complex Internet of Things (IoT) devices.

HySecTwin introduces a knowledge-driven digital twin framework that uses semantic modeling and hybrid reasoning to provide explainable, context-aware, and high-speed threat detection for complex Cyber…

This paper investigates the vulnerability of machine learning-based fault detection and localization systems in Cyber-Physical Systems (CPS) to backdoor attacks, demonstrating that such attacks are su…

The paper develops a unified mathematical framework to analyze the interaction between post-quantum security, real-time communication constraints, and closed-loop stability in safety-critical turbofan…

This paper demonstrates that using a communication algorithm (CommFormer) with heterogeneous agents significantly improves the speed and performance of multi-agent reinforcement learning for autonomou…

The paper introduces the Canonical Security Telemetry Substrate (CSTS), a standardized, AI-ready foundation designed to harmonize fragmented and heterogeneous cybersecurity data into a unified model f…

This paper reviews advanced AI-based solutions, specifically combining LLMs and RL, to create dynamic and cost-effective network and device-level cyber deception strategies for contested environments.

The paper introduces CritBench, a novel framework to evaluate LLM cybersecurity capabilities specifically within IEC 61850 Digital Substation Operational Technology (OT) environments, finding that whi…

This survey comprehensively analyzes the IoT threat landscape by detailing 28 common attacks and mapping them to foundational vulnerability classes, providing a structured roadmap for building secure…

This paper introduces Bit2Watt, a vulnerability in which an adversary manipulates GPU workloads to destabilize local power infrastructure and disrupt computing services, operating entirely within the…

This paper discusses the significant challenges in developing a holistic intrusion detection system for Industrial Control Systems (ICS) that must cover all operational dimensions.

The paper proposes a Digital Twin (DT)-driven hybrid system that combines deterministic heuristics and constrained Large Language Model (LLM) reasoning to achieve highly accurate and interpretable rea…

This paper experimentally demonstrates that IEC 61850 Sampled Values-based protection systems are vulnerable to stealthy, coordinated False Data Injection Attacks (FDIAs) that can disrupt grid protect…

This paper proposes a hybrid CNN-LSTM framework to enhance cyber attack detection and prevention in U.S. critical digital infrastructure by evaluating multiple machine learning models on the CSE-CIC-I…

The paper proposes EnThM, a lightweight, hierarchical verification scheme that uses statistical and rule-based checks on aggregated metering data to mitigate real-time power theft in smart grids.

This paper proposes a decentralized, blockchain-based protocol using both public and private blockchains to enhance the cyber resilience and security of IoT data transfer for digital building twins an…