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 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 proposes a Cycle-Space Detector (CSD) that uses network topology constraints to effectively detect stealthy, data-driven False Data Injection Attacks (FDIA) that exploit the null space of me…

The paper proposes a Quantum Augmented Microgrid (QuAM) framework that integrates quantum networking concepts to enhance the cybersecurity, confidentiality, and privacy of decentralized microgrids aga…

This paper demonstrates that Unmanned Aerial Vehicle (UAV) autopilot fail-safe mechanisms are vulnerable to non-invasive voltage glitch fault injection, potentially allowing attackers to suppress crit…

The paper introduces GenAI-FDIA, a comprehensive framework that benchmarks various physics-informed generative models to synthesize high-fidelity False Data Injection Attacks (FDIA) for power systems,…

This paper evaluates unsupervised temporal learning models, specifically recurrent autoencoders, for real-time anomaly detection in vulnerable IEC-61850 GOOSE networks, demonstrating that the GRU mode…

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.

The paper proposes RTS-ABAC, a novel real-time server-aided Attribute-Based Access Control mechanism designed to secure time-critical communications in substation automation systems, achieving low-lat…

The paper introduces i-SDT, an intelligent Self-Defending Digital Twin, which enhances cyber-physical security by accurately discriminating various attack types and maintaining safe operation without…

The paper proposes a novel three-layer metric framework to comprehensively evaluate quantum circuit integrity by combining structural, operational, and interaction-level analyses, demonstrating that n…

This paper reviews the current state of cybersecurity for EV charging infrastructure, analyzing existing machine learning countermeasures and proposing future directions to overcome data limitations i…

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 demonstrates that an off-the-shelf Large Language Model (LLM) can function as a high-performing, explainable, human-in-the-loop layer for detecting cyberattacks in Industrial Control System…

The paper proposes DAIRE, a lightweight AI model, for highly efficient, real-time detection and classification of various cyberattacks targeting the vulnerable Controller Area Network (CAN) in the Int…

FlowGuard introduces an identity-independent defense using flow matching to detect data-free model stealing attacks by identifying synthetic queries as out-of-distribution based on their lower-dimensi…

This paper proposes a lightweight, multi-layer Machine Learning-based security framework for Industrial IoT (IIoT) to enhance trust convergence and detect advanced threats.

The paper proposes a system-aware unsupervised framework that combines lightweight online detection with a contextual digital twin and LLM to provide interpretable, actionable anomaly diagnoses for In…

This paper identifies and demonstrates eight novel attack scenarios exploiting the ISO 15765-2 transport protocol over CAN, showing that three can successfully induce denial of diagnostic services in…