arXiv AI

Digital Twin Degradation: Detecting Cyber Physical Attacks via Temporal Inconsistencies

arXiv:2608. 16159v1 Announce Type: cross Abstract: Digital Twins (DTs) are increasingly used to monitor and analyze Cyber Physical Systems (CPS).

arXiv Machine Learning

Jun 10

Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention

arXiv:2603. 10676v2 Announce Type: replace Abstract: Industrial Control Systems (ICS) underpin critical infrastructure and face growing cyber-physical threats due to the convergence of operational technology and networked environments.

By Kosti Koistinen, Kirsi Hellsten, Joni Herttuainen, Kimmo K. Kaski

arXiv AI

Jun 26

Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT

arXiv:2601. 01701v2 Announce Type: replace-cross Abstract: Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, and efficiency of industrial systems.

By Mohammed Ayalew Belay, Adil Rasheed, Pierluigi Salvo Rossi

efficiency safety

arXiv Machine Learning

Jun 30

In-Vehicle Digital Twin-Based Collision Warning Framework with Sybil Attack Detection

arXiv:2606. 28625v1 Announce Type: cross Abstract: Connected Vehicles (CVs) rely extensively on communication technologies to enable data-driven predictive analyses for enhancing performance and safety.

By Mohammad Imtiaz Hasan, Abyad Enan, Jean Michel Tine, Araf Rahman, M Sabbir Salek, Mashrur Chowdhury

safety

arXiv AI

6d ago

Towards Model-based Run-time Cybersecurity: On Control-Flow Anomaly Detection, Attack Identification, and Hardware Monitoring

arXiv:2608. 11802v1 Announce Type: cross Abstract: Methods to increase the resilience of systems to cyber-attacks become increasingly important.

By Martin Sachenbacher, Martin Leucker, Alexander Weiss, Aliyu Tanko Ali

arXiv Machine Learning

Jun 5

Attack Detection using Time Series Foundation Models

arXiv:2606. 06347v1 Announce Type: cross Abstract: This paper addresses the problem of attack detection in cyber-physical systems without any knowledge of the plant model or its structure.

By Sribalaji C. Anand, Anh Tung Nguyen, George J. Pappas

arXiv AI

1d ago

Hierarchical Agentic Incident Response with Digital-Twin-Validated Attack Inference

arXiv:2608. 15016v1 Announce Type: cross Abstract: Network incident response remains slow and labor-intensive as the defender must infer multi-stage attacks from partial observations and translate recovery decisions into reliable system commands.

By Yiran Gao, Juntao Chen, Tao Li

llms agents fine-tuning

arXiv Machine Learning

Jun 2

IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems

arXiv:2606. 01691v1 Announce Type: cross Abstract: Industrial Internet systems face increasing threats from sophisticated industrial control system (ICS) attacks, resulting in critical safety incidents.

By Yuchen Zhang, Ning Xi, Pengbin Feng, Shigang Liu, Jianfeng Ma, Yulong Shen, Yanan Sun, Xiaolin Zhou

llms robotics multimodal benchmarks safety

arXiv AI

Jun 2

Product-Aware Deep Autoencoders for Robust Process Monitoring in Multi-Product Cyber-Physical Systems

arXiv:2606. 00052v1 Announce Type: new Abstract: As Industry 4.

By MD Shafikul Islam, Jordan Carden

benchmarks safety

arXiv Machine Learning

Jun 25

A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids

arXiv:2606. 25200v1 Announce Type: new Abstract: The accelerated digitalization of renewable energy smart grids through IoT sensors, AMI, and SCADA systems has significantly expanded the attack surface for sophisticated cyberattacks, FDI attacks that stealthily distort state estimation and DoS/DDoS attacks that flood communication channels.

By Sajib Debnath, Remon Das

efficiency benchmarks

arXiv AI

Jun 6

Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework

arXiv:2606. 05710v1 Announce Type: cross Abstract: The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased exposure to advanced cyber adversaries and operational vulnerabilities.

By B. M. Taslimul Haque, Md. Arifur Rahman, Md. Serajul Kabir Chowdhury Rubel, Md. Iqbal Hossan

safety

arXiv Machine Learning

Jun 19

Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids

arXiv:2606.

By Farhin Farhad Riya, Shahinul Hoque, Yingyuan Yang, Jinyuan Sun, Kevin Tomsovic

safety

arXiv AI

6d ago

AgenticTwin: An Agentic LLM Framework Integrated with Digital Twin for Anomaly Detection

arXiv:2608. 11679v1 Announce Type: new Abstract: Digital twins are increasingly used to monitor and simulate the behavior of cyber-physical systems.

By Touseef Hasan, Mounika Ghanta, Souvika Sarkar, Ujjwal Guin

llms agents benchmarks

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