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 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 an AI-based supervisory layer using a recurrent neural network to validate the physical integrity of current measurements used by line current differential relays in inverter-based…

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…

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 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 evaluates quantum machine learning for detecting anomalies in UAVs using a rigorous, leakage-free methodology, showing that a hybrid XGBoost + Data Reuploading classifier performs well, part…

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…

AEGIS introduces a novel physics-based system that analyzes encrypted network traffic flow dynamics, achieving state-of-the-art zero-day evasion detection with high accuracy and low latency.

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…

The paper introduces the Black-Hole Attack, a poisoning vulnerability that exploits geometric defects in high-dimensional embedding spaces to force malicious vectors into the top-k results of vector d…

The paper introduces a Spatiotemporal-Aware Fault Injection (STAFI) framework to efficiently locate and time critical bit-flip vulnerabilities in DNNs used for ADAS, significantly improving fault dete…

The paper proposes using geometric metrics, specifically eigenspace alignment, to monitor the structural integrity of large behavioral populations, demonstrating its effectiveness in detecting network…

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…

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

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…

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…

The paper introduces the concept of 'host-space perturbations,' arguing that real-world attackers can only manipulate network inputs by controlling specific hosts, a constraint that significantly weak…

The paper introduces 'adversarial restlessness,' an activation-level signature in LLM residual streams, to detect multi-turn prompt injection attacks with high accuracy.