amlsec

Automated Security Risk Identification in Cyber-Physical Production Systems Using AutomationML-Based Engineering Data This prototype implements the methods presented in the following two publications: 1. Eckhart, M., Ekelhart, A., & Weippl, E. R. 2020. Automated Security Risk Identification Using...

minicps

MiniCPS MiniCPS is a framework for Cyber-Physical Systems real-time simulation. It includes support for physical process and control devices simulation, and network emulation. It is build on top of mininet. MiniCPS is developed by the SCy-Phy group from SUTD Singapore University of Design and...

cve-2025-2082

How to Hack a Tesla? — Interactive Scenario Application This project is an interactive cybersecurity scenario based on a real vulnerability discovered by the security firm Synacktiv TPMS → VCSEC → CAN Bus chain. The scenario aims to teach how a Tesla Model 3 could be compromised through a...

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,...

MimicSat: A Reconfigurable Cyber-Physical Testbed for Small Satellite Systems and Cybersecurity Research

MimicSat provides a common experimental environment for examining how changes in satellite subsystem behavior propagate to mission outcomes across software-based and hardware-based execution. Its design is motivated by controlled spacecraft cybersecurity studies involving attacks, faults, and...

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...

Wicked Problem, Parsimonious Solution: Securing Electric Vehicle Charging Station Software

Electric vehicle charging infrastructure presents a suite of novel cyber-physical threats. Among this infrastructure, charging stations are the most vulnerable elements. The software in the charging station supply equipment is particularly vulnerable. Currently, the software is an attack surface...

AdaptoNet: Modular Foundation-Adaptive Neural Networks for Cyber-Physical Attack Detection in Power Grids

Cyber attacks on the power grid combine physical disruptions with compromised data to destabilize cyber-physical systems. We demonstrate that data denial attacks, where adversaries block measurements in a targeted region while triggering a line outage, reduce detection performance by more than...

Neuro-Agentic Control: A Deep Learning-Based LLM-Powered Agentic AI Framework for Controlling Security Controls

Cyberattacks on operational technology are increasingly causing costly downtime and physical damage, exposing the limitations of traditional rule-based monitoring in industrial IoT environments. While Large Language Models LLMs have strong semantic reasoning abilities to assist in decision suppor...

Bit2Watt: A Cyber-Physical Vulnerability Exploiting GPU Workloads across Power and Computing Infrastructures

Modern data centers increasingly rely on large-scale GPU clusters and on-site renewable energy resources, resulting in a tightly coupled cyber-physical system between computing workloads and power-electronic-dominated grids. In this paper, we reveal Bit2Watt, a previously unexplored vulnerability...

AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework

AI systems are increasingly evaluated in bounded environments that combine isolation, simulation, instrumentation, supervision, and evidence capture. For physical AI, AIoT, and cyber-physical systems, this shift is not a matter of terminology: the system under test may sense, decide, actuate,...

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...

On the Challenges of Holistic Intrusion Detection in ICS

Past attacks against industrial control systems ICS show that adversaries often target both the ICS network and the physical process to achieve potential catastrophic impact. To secure ICS, intrusion detection systems promise timely uncovering of such adversaries. However, as these detection...

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...

TrendAI™ Research at RSAC 2026: Advancing Defense Across AI‑Driven and Cyber‑Physical Threats

TrendAI™ Research explored agentic AI cybercrime and EV infrastructure security through two research sessions at RSAC 2026...

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...

Cyber-Resilient Digital Twins: Discriminating Attacks for Safe Critical Infrastructure Control

Industrial Cyber-Physical Systems ICPS face growing threats from cyber-attacks that exploit sensor and control vulnerabilities. Digital Twin DT technology can detect anomalies via predictive modelling, but current methods cannot distinguish attack types and often rely on costly full-system...