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…

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 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…

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…

The paper introduces a threat-oriented digital twinning methodology to enable reproducible and controllable cybersecurity evaluation of autonomous platforms, overcoming limitations in accessing real-w…

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…

IstGPT introduces a novel LLM-based framework for real-time, fine-grained anomaly detection in complex industrial cyber-physical systems, achieving state-of-the-art performance across multiple benchma…

The paper demonstrates that standard homomorphic encryption (HE) schemes are insufficient to guarantee integrity in networked control systems (NCS) against covert attacks, proposing instead a verifiab…

This paper introduces a foundational framework and taxonomy for managing catastrophic AI loss of control (LOC) incidents, providing a proportional guide for response based on the severity and recovera…

This paper demonstrates that neural operators used in digital twins for nuclear systems are highly vulnerable to undetectable, sparse adversarial perturbations, necessitating new robustness guarantees…

This paper provides the first systematic threat analysis of State-Space Models (SSMs) in safety-critical applications, introducing novel attack classes and formal metrics to quantify their security an…

The paper proposes a tool-mediated LLM architecture for autonomous cyber defense, formally proving its stability and demonstrating that it significantly reduces an attacker's expected payoff in real-w…

The paper proposes a federated, high-throughput stream-processing framework for cross-sector threat detection and automated containment, achieving end-to-end operational convergence within 12-20 secon…

The paper introduces C-MADF, a causally constrained multi-agent framework that significantly reduces false positives in autonomous cyber defense by restricting response actions to structurally consist…

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…

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 proposes a semi-automated framework that integrates network topology and vulnerability data to generate and analyze multi-step attack graphs in Industrial Control Systems, demonstrated using…

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 two-timescale governance framework using a multi-agent LLM to safely update and guide RL agents for SDN-IoT defense, significantly improving performance and stability under advers…

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…