awesome-industrial-control-system-security

awesome-industrial-control-system-security A curated list of resources related to Industrial Control System ICS security. Feel free to contribute. Tools AttkFinder | AttkFinder is a tool that performs static program analysis of PLC programs, and produce Data-oriented Attack vectors. In a nutshell...

amlsec

Identificación Automatizada de Riesgos de Seguridad en Sistemas de Producción Ciberfísicos Utilizando Datos de Ingeniería Basados en AutomationML Este prototipo implementa los métodos presentados en las siguientes dos publicaciones: 1. Eckhart, M., Ekelhart, A., & Weippl, E. R. 2020. Automated...

LogiC-Diff: Embedding Security Properties into AI-Enabled Cyber-Physical Systems

AI-enabled Cyber-Physical Systems CPS are highly vulnerable to adversarial and anomalous inputs, where small perturbations can induce cascading errors and unsafe control actions. Existing approaches, such as rule-based filtering, training-time regularization, or diffusion-based reconstruction,...

Safety-Aware Zero Trust Enforcement for IoT and Cyber-Physical Systems

Zero Trust ZT replaces the implicit trust of perimeter-based security with explicit, continuous, context-aware authorization. This shift is particularly relevant to IoT and cyber-physical systems, whose heterogeneous, long-lived, and remotely connected components make persistent trust untenable...

Graph Neural Networks at War: Integrating Cybersecurity and Drone Intelligence in the Israeli-Iranian Conflict

Physical cyber systems have brought about new threats and challenges in detection and immediate response. This study examines how Graph Neural Networks GNNs can be used to aid cybersecurity and drone management in a physical cyber system comprising of cyber intrusions and unmanned aerial vehicles...

Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systems

Cyber-Physical Systems CPS integrate sensing, communication, computation, and control to support critical infrastructure, including smart grids, industrial automation, and control systems. In the electrical utility domain, various controllers are used in CPS to ensure the system detects and...

SMSI: System Model Security Inference: Automated Threat Modeling for Cyber-Physical Systems

Threat modeling for cyber-physical systems CPS remains a largely manual exercise. This project presents SMSI System Model Security Inference, a hybrid neuro-symbolic pipeline that starts from a SysML architecture model and produces a prioritized list of NIST 800-53 security controls. The prototyp...

From Incomplete Architecture to Quantified Risk: Multimodal LLM-Driven Security Assessment for Cyber-Physical Systems

Cyber-physical systems often contend with incomplete architectural documentation or outdated information resulting from legacy technologies, knowledge management gaps, and the complexity of integrating diverse subsystems over extended operational lifecycles. This architectural incompleteness...

Secure Reinforcement Learning: On Model-Free Detection of Man in the Middle Attacks

We consider the problem of learning-based man-in-the-middle MITM attacks in cyber-physical systems CPS, and extend our previously proposed Bellman Deviation Detection BDD framework for model-free reinforcement learning RL. We refine the standard MDP attack model by allowing the reward function to...

Supporting Artifact Evaluation with LLMs: A Study with Published Security Research Papers

Artifact Evaluation AE is essential for ensuring the transparency and reliability of research, closing the gap between exploratory work and real-world deployment is particularly important in cybersecurity, particularly in IoT and CPSs, where large-scale, heterogeneous, and privacy-sensitive data...

Secure Group Key Agreement on Cyber-Physical System Buses

Cyber-Physical Systems CPSs rely on distributed embedded devices that often must communicate securely over buses. Ensuring message integrity and authenticity on these buses typically requires group-shared keys for Message Authentication Codes MACs. To avoid insecure fixed pre-shared keys and...

Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundations

Cybersecurity is being fundamentally reshaped by foundation-model-based artificial intelligence. Large language models now enable autonomous planning, tool orchestration, and strategic adaptation at scale, challenging security architectures built on static rules, perimeter defenses, and...

RampoNN: A Reachability-Guided System Falsification for Efficient Cyber-Kinetic Vulnerability Detection

Detecting kinetic vulnerabilities in Cyber-Physical Systems CPS, vulnerabilities in control code that can precipitate hazardous physical consequences, is a critical challenge. This task is complicated by the need to analyze the intricate coupling between complex software behavior and the system's...

SAFER-D: a Self-Adaptive Security Framework for Distributed Computing Architectures

The rise of the Internet of Things and Cyber-Physical Systems has introduced new challenges on ensuring secure and robust communication. The growing number of connected devices increases network complexity, leading to higher latency and traffic. Distributed computing architectures DCAs have gaine...

Advancing Security with Digital Twins: a Comprehensive Survey

The proliferation of electronic devices has greatly transformed every aspect of human life, such as communication, healthcare, transportation, and energy. Unfortunately, the global electronics supply chain is vulnerable to various attacks, including piracy of intellectual properties, tampering,...

Federated Learning for Cyber Physical Systems: a Comprehensive Survey

The integration of machine learning ML in cyber physical systems CPS is a complex task due to the challenges that arise in terms of real-time decision making, safety, reliability, device heterogeneity, and data privacy. There are also open research questions that must be addressed in order to ful...

Data-Driven Falsification of Cyber-Physical Systems

Whitepaper called Data-Driven Falsification Of Cyber-Physical Systems...

Monitor and Recover: a Paradigm for Future Research on Distribution Shift in Learning-Enabled Cyber-Physical Systems

With the known vulnerability of neural networks to distribution shift, maintaining reliability in learning-enabled cyber-physical systems poses a salient challenge. In response, many existing methods adopt a detect and abstain methodology, aiming to detect distribution shift at inference time so...

Cyber Signals: Risks to critical infrastructure on the rise

Today, the third edition of Cyber Signals was released spotlighting security trends and insights gathered from Microsoft’s 43 trillion daily security signals and 8,500 security experts. In this edition, we share new insights on wider risks that converging IT, Internet of Things IoT, and operation...

Cyber Signals: Risks to critical infrastructure on the rise

Today, the third edition of Cyber Signals was released spotlighting security trends and insights gathered from Microsoft’s 43 trillion daily security signals and 8,500 security experts. In this edition, we share new insights on wider risks that converging IT, Internet of Things IoT, and operation...