Dataset of anomalies and malicious acts in a cyber-physical subsystem

Analysis of quality measurements to categorize anomalies in sensor systems

Laso, P. M., Brosset, D., & Puentes, J. (2017) · 2017 Computing Conference · doi:10.1109/sai.2017.8252263 · Cited by 15 · see citing works

An Industrial Control System Testbed Based on Emulation, Physical Devices and Simulation

Gao, H., Peng, Y., Dai, Z., Wang, T., Han, X., & Li, H. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-45355-1_6 · Cited by 14 · see citing works

Monitoring Approach of Cyber-Physical Systems by Quality Measures

Laso, P. M., Brosset, D., & Puentes, J. (2017) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-61563-9_9 · Cited by 10 · see citing works

Sensor Systems and Software

Bilas, V., Ferrero, F., & Magno, M. (2017) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-61563-9 · Cited by 5 · see citing works

Cyber risk and cybersecurity: a systematic review of data availability

Cremer, F., Sheehan, B., Fortmann, M., Kia, A. N., Mullins, M., Murphy, F., & Materne, S. (2022) · The Geneva Papers on Risk and Insurance Issues and Practice · doi:10.1057/s41288-022-00266-6 · Cited by 437 · see citing works

A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems

Hindy, H., Brosset, D., Bayne, E., Seeam, A. K., Tachtatzis, C., Atkinson, R. C., & Bellekens, X. (2020) · IEEE Access · doi:10.1109/access.2020.3000179 · Cited by 283 · see citing works

Federated Learning-Based Explainable Anomaly Detection for Industrial Control Systems

Hương, T. T., Bac, T. P., Ha, K. N., Việt, N. H., Hoang, N. X., Hung, N. T., & Tran, K. P. (2022) · IEEE Access · doi:10.1109/access.2022.3173288 · Cited by 126 · see citing works

Advancing Network Security in Industrial IoT: A Deep Dive into AI-Enabled Intrusion Detection Systems

Shahin, M., Maghanaki, M., Hosseinzadeh, A., & Chen, F. F. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102685 · Cited by 119 · see citing works

Multi-Source Multi-Domain Data Fusion for Cyberattack Detection in Power Systems

Sahu, A., Mao, Z., Wlazlo, P., Huang, H., Davis, K., Goulart, A., & Zonouz, S. (2021) · IEEE Access · doi:10.1109/access.2021.3106873 · Cited by 114 · see citing works

A Hardware-in-the-Loop Water Distribution Testbed Dataset for Cyber-Physical Security Testing

Faramondi, L., Flammini, F., Guarino, S., & Setola, R. (2021) · IEEE Access · doi:10.1109/access.2021.3109465 · Cited by 104 · see citing works

A novel approach for accurate detection of the DDoS attacks in SDN-based SCADA systems based on deep recurrent neural networks

Polat, H., Türkoğlu, M., Polat, O., & Şengür, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116748 · Cited by 86 · see citing works

A Survey of Industrial AIoT: Opportunities, Challenges, and Directions

Awaisi, K. S., Ye, Q., & Sampalli, S. (2024) · IEEE Access · doi:10.1109/access.2024.3426279 · Cited by 88 · see citing works

Anomaly events classification and detection system in critical industrial internet of things infrastructure using machine learning algorithms

Selim, G. E. I., Hemdan, E. E., Shehata, A. M., & El-Fishawy, N. A. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10354-1 · Cited by 76 · see citing works

Industrial cyber-physical systems protection: A methodological review

Canonico, R., & Sperlí, G. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103531 · Cited by 62 · see citing works

Integrated management of safety and security in Seveso sites - sociotechnical perspectives

Ylönen, M., Tugnoli, A., Oliva, G., Heikkilä, J., Nissilä, M., Iaiani, M., Cozzani, V., Setola, R., Assenza, G., van der Beek, D., et al. (2022) · Safety Science · doi:10.1016/j.ssci.2022.105741 · Cited by 41 · see citing works

Super learner ensemble for anomaly detection and cyber-risk quantification in industrial control systems

Ahmadi-Assalemi, G., Al-Khateeb, H., Epiphaniou, G., & Aggoun, A. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3144127 · Cited by 34 · see citing works

Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning

Hindy, H., Brosset, D., Bayne, E., Seeam, A., & Bellekens, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-12786-2_1 · Cited by 29 · see citing works

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems

Oyucu, S., Polat, O., Türkoğlu, M., Polat, H., Aksöz, A., & AĞDAŞ, M. T. (2023) · Sensors · doi:10.3390/s24010155 · Cited by 27 · see citing works

Improving SIEM for critical SCADA water infrastructures using machine learning

Hindy, H., Brosset, D., Bayne, E., Seeam, A. K., & Bellekens, X. (2019) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · Cited by 22 · see citing works

Securing the cyber-physical system: a review

Lydia, M., Kumar, G. E. P., & Selvakumar, A. I. (2022) · Cyber-Physical Systems · doi:10.1080/23335777.2022.2104378 · Cited by 24 · see citing works

ACWA: an AI-driven cyber-physical testbed for intelligent water systems

Batarseh, F. A., Kulkarni, A., Sreng, C., Lin, J., & Maksud, S. (2023) · Water Practice & Technology · doi:10.2166/wpt.2023.197 · Cited by 21 · see citing works

An IIoT Based ICS to Improve Safety Through Fast and Accurate Hazard Detection and Differentiation

Moradbeikie, A., Jamshidi, K., Bohlooli, A., García, J., & Masip‐Bruin, X. (2020) · IEEE Access · doi:10.1109/access.2020.3037093 · Cited by 21 · see citing works

Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices

Audi, U. I., Danyaro, K. U., Muazu, A. A., & Maiwada, U. D. (2024) · IEEE Access · doi:10.1109/access.2024.3369915 · Cited by 21 · see citing works

Cyber Security Using Machine Learning: Techniques and Business Applications

Gupta, A., Gupta, R., & Kukreja, G. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-72080-3_21 · Cited by 17 · see citing works

Machine Learning for Threat Recognition in Critical Cyber-Physical Systems

Perrone, P., Flammini, F., & Setola, R. (2021) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr51186.2021.9527979 · Cited by 16 · see citing works

A MEC-IIoT intelligent threat detector based on machine learning boosted tree algorithms

Ruiz-Villafranca, S., Roldán-Gómez, J., Carrillo-Mondéjar, J., Gómez, J. M. C., & Villalon, J. M. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.109868 · Cited by 16 · see citing works

An efficient machine learning model for malicious activities recognition in water‐based industrial internet of things

Selim, G. E. I., Hemdan, E. E., Shehata, A. M., & El-Fishawy, N. A. (2021) · Security and Privacy · doi:10.1002/spy2.154 · Cited by 15 · see citing works

Evaluating Machine Learning Approaches for Cyber and Physical Anomalies in SCADA Systems

Faramondi, L., Flammini, F., Guarino, S., & Setola, R. (2023) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr57506.2023.10224915 · Cited by 14 · see citing works

Security Aspects of Cyber Physical Systems

Ahmad, I., Zarrar, M. K., Saeed, T., & Rehman, S. (2018) · International Conference on Computer Applications & Information Security (ICCAIS) · doi:10.1109/cais.2018.8442009 · Cited by 14 · see citing works

AI for Cyberbiosecurity in Water Systems—A Survey

Sobien, D., Yardimci, M. O., Nguyen, M. B. T., Mao, W.-Y., Fordham, V., Rahman, A., Duncan, S. E., & Batarseh, F. A. (2023) · Springer eBooks · doi:10.1007/978-3-031-26034-6_13 · Cited by 13 · see citing works

Using machine learning to detect network intrusions in industrial control systems: a survey

Termanini, A., Al‐Abri, D., Bourdoucen, H., & Maashri, A. A. (2024) · International Journal of Information Security · doi:10.1007/s10207-024-00916-x · Cited by 12 · see citing works

AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review

Seo, J. K., Lee, J., Kim, B., Shim, W., & Seo, J. T. (2025) · Electronics · doi:10.3390/electronics15010020 · Cited by 15 · see citing works

Conformal machine learning for reliable anomaly detection in industrial cyber-physical systems

Yuan, S., Li, J., Wang, C., & Zhang, X. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112417 · Cited by 11 · see citing works

P 2 O: AI-Driven Framework for Managing and Securing Wastewater Treatment Plants

Kulkarni, A., Yardimci, M., Sikder, M. N. K., & Batarseh, F. A. (2023) · Journal of Environmental Engineering · doi:10.1061/joeedu.eeeng-7266 · Cited by 11 · see citing works

Opportunities and Challenges of Using Artificial Intelligence in Securing Cyber-Physical Systems

Nweke, L. O., & Yayilgan, S. Y. (2024) · Springer eBooks · doi:10.1007/978-3-031-57452-8_7 · Cited by 11 · see citing works

Adaptive learning anomaly detection and classification model for cyber and physical threats in industrial control systems

Ahmadi‐Assalemi, G., Al‐Khateeb, H., Benson, V., Adamyk, B., & Ammi, M. (2025) · IET Cyber-Physical Systems: Theory & Applications · doi:10.1049/cps2.70004 · Cited by 10 · see citing works

Explainable Articial Intelligence for Cybersecurity in Smart Manufacturing

Bac, T. P., Ha, D. T., Tran, K. D., & Tran, K. P. (2023) · Springer series in reliability engineering · doi:10.1007/978-3-031-30510-8_10 · Cited by 8 · see citing works

Anomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges

Kuchař, K., & Fujdiak, R. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3512857 · Cited by 11 · see citing works

Naval cyber-physical anomaly propagation analysis based on a quality assessed graph

Pelissero, N., Laso, P. M., & Puentes, J. (2020) · International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) · doi:10.1109/cybersa49311.2020.9139634 · Cited by 6 · see citing works

Few-shot learning for modeling cyber physical systems in non-stationary environments

Ntalampiras, S., & Potamitis, I. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07903-0 · Cited by 4 · see citing works

An Empirical Comparison on Malicious Activity Detection Using Different Neural Network-Based Models

Albahar, M. A., Al-Falluji, R. A., & Binsawad, M. H. (2020) · IEEE Access · doi:10.1109/access.2020.2984157 · Cited by 4 · see citing works

Survey and Future Trends for Cybersecurity in Maritime and Port Sectors: A Discrete Event Systems Perspective

Liu, G., Amri, O., Liang, Y., Zhang, Z., Laso, P. M., Bertelle, C., Berred, A. M., & Lefebvre, D. (2025) · Mathematics · doi:10.3390/math13223650 · Cited by 3 · see citing works

Optimal Stealth Attacks to Cyber-Physical Systems: Seeking a Compromise between Maximum Damage and Effort*

Faramondi, L., Oliva, G., & Setola, R. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.01.082 · Cited by 2 · see citing works

Supporting Cyber-Attacks and System Anomaly Detection Research with an Industry 4.0 Dataset

Shi, L., Krishnan, S., Wen, S., & Xiang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23020-2_19 · Cited by 1 · see citing works

Intrusion Detection Datasets for Cyber-Physical Systems: Taxonomy, Challenges, and OpportunitiesPreprint

Quincozes, V. E., Quincozes, S. E., Neves, C. D. A. V., Passos, D. G., & Mossé, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5247519 · Cited by 1 · see citing works

Visualization of regional resource advantage measurement based on physical network data analysis

Li, W. (2021) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces51350.2021.9489089

Towards Securing Water Systems Communications from Adversarial Attacks using AI Assurance

Yardimci, M. O., & Batarseh, F. A. (2025) · IEEE Middle East and North Africa Communications Conference (MENACOMM) · doi:10.1109/menacomm62946.2025.10911020

Machine Learning for Industrial Iot Cybersecurity: A Systematic ReviewPreprint

Dohin, A., Zkik, K., Akilal, A., Omar, M., & Akli, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5388573

Détection de dysfonctionements et d'actes malveillants basée sur des modèles de qualité de données multi-capteurs

Laso, P. M. (2017) · doi:10.70675/2097494ez4169z4eaez8a84zf56b536257fe

Artificial Intelligence, Digital Twins, and Cyber-Resilience in Water Distribution Systems: A Critical Narrative Review with a WDT Benchmark Case Study

Pérez-Pérez, E.-J. (2026) · Eng—Advances in Engineering · doi:10.3390/eng7100564

Intrusion detection datasets for Cyber–Physical Systems: Taxonomy, challenges, and opportunities

Quincozes, V. E., Quincozes, S. E., Celio, A., Passos, D. G., & Mossé, D. (2026) · Computers & Security · doi:10.1016/j.cose.2026.105217

SHoRT-CA: Smart Home Data Repository of Time Series with Contextual Anomalies

Toor, A. A., Lin, J., & Gran, E. G. (2026) · Data in Brief · doi:10.1016/j.dib.2026.113370

Detection of Cyberattacks and Anomalies in Cyber-Physical Systems: Approaches, Data Sources, Evaluation

Tushkanova, O. N., Levshun, D., Branitskiy, A. A., Владимировна, Ф. Е., Novikova, E. S., & Kotenko, I. V. (2023) · Algorithms · doi:10.3390/a16020085 · Cited by 61 · see citing works

Anomaly Detection for Cyber-Physical Systems

Schneider, P., Eckert, C., & München, T. U. (2021)

Towards the Testbed and Dataset for Analysis of Water Treatment Systems Security

Владимировна, Ф. Е., Novikova, E. S., Danilov, A., & Saenko, I. B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-7814-4_37 · Cited by 8 · see citing works

The Diagnosis of Faults in Cyber-Physical Systems: a scoping study

Dowdeswell, B. (2018) · Data Archiving and Networked Services (DANS) · doi:10.17632/72xjbt6rkp.2

Methodology for detecting abnormal operation of cyber-physical systems

Basan, Е. S., Bogdanovich, M. A., Dmitrievna, M. V., & Basan, A. S. (2024) · All-Russian Scientific Conference "System Synthesis and Applied Synergetics" · doi:10.18522/syssyn-2024-67