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…

The FALCON-C framework proposes a flow-based autoencoder approach to detect cyber anomalies and label malicious flows in connected vehicular networks, achieving high accuracy in identifying attacks on…

This paper surveys how integrating Edge Computing, Machine Learning, and Deep Learning can enhance the security and resilience of complex Internet of Vehicles (IoV) networks.

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…

This paper evaluates the security of industrial control systems (ICS) transitioning to 5G communication, finding that while optimal conditions allow for resilience, degraded channel conditions signifi…

The paper introduces a novel pipeline integrating formal verification and process mining to systematically identify and analyze root causes of security property invalidations in complex automotive net…

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 proposes CyberAId, a hybrid multi-agent system designed to enhance cybersecurity for financial institutions by integrating specialized LLM subagents with existing SIEM/XDR telemetry, address…

This paper evaluates the vulnerability of leading LLMs deployed in smart grid operations to jailbreaking attacks, finding that while some models show high susceptibility, Claude 3.5 Haiku demonstrated…

This paper analyzes darknet traffic to characterize advanced, AI-assisted bot reconnaissance, finding that modern evasion techniques allow most bot traffic to bypass standard IDS thresholds.

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 provides the first comprehensive threat model for IoT-enabled Controlled Environment Agriculture (CEA) systems, identifying 123 unique threats and proposing a defense-in-depth framework to…

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

This paper demonstrates a non-disruptive, sidecar-based integration of NIST-standardized Post-Quantum Cryptography (PQC) into an open-source 5G core, showing that while it introduces a predictable lat…

The paper develops a novel, resource-aware cybersecurity risk assessment framework specifically tailored for power-limited CubeSat missions, demonstrating that adapting controls can significantly impr…

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…

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 systematically evaluates modern security logging standards (CIM, OCSF, ECS) using a novel framework to quantify their detection efficacy across diverse exploit scenarios, revealing critical…

The paper models how AI-driven data center demand stresses the electrical grid, finding that relying solely on renewable energy certificates (RECs) is insufficient and that on-site storage and spatial…