{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T05:06:42Z","timestamp":1788930402560,"version":"build-2803163510"},"reference-count":35,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The rapid deployment of Internet of Things (IoT)-enabled electric vehicle (EV) charging infrastructures introduces cybersecurity challenges due to distributed operation, communication dependency, and exposure to malicious cyberattacks. Existing anomaly detection approaches mainly rely on centralized learning architectures that require raw charging data, creating privacy concerns, scalability limitations, and reduced robustness against stealth cyberattacks that mimic legitimate charging behavior. To address these issues, this paper proposes a privacy-preserving federated cybersecurity framework for IoT-based EV charging infrastructures using distributed anomaly detection. The framework integrates cybersecurity-aware feature engineering, aggressive and stealth cyberattack simulation, centralized anomaly detection, and federated learning without raw charging session sharing. A real-world EV charging dataset is utilized to model legitimate and malicious charging behavior using operational and engineered features, including charging ratio and power consistency. Experimental evaluation compares Isolation Forest, centralized LSTM autoencoder, and federated LSTM anomaly detection under aggressive and stealth cyberattack scenarios. The results show that aggressive cyberattacks are more easily separable, where Isolation Forest achieves an attack F1-score of 0.85 and AUC of 0.86, while stealth cyberattacks reduce detection performance, lowering the F1-score to 0.61. The centralized LSTM autoencoder demonstrates superior detection performance, whereas the federated LSTM preserves privacy and achieves competitive results with 0.86 accuracy, 0.67 attack F1-score, and 0.81 AUC without transmitting raw charging data. These findings demonstrate federated anomaly detection as a promising privacy-preserving solution for secure EV charging infrastructures.<\/jats:p>","DOI":"10.3390\/s26175541","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T03:19:26Z","timestamp":1788232766000},"page":"5541","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Federated Anomaly Detection for IoT-Enabled EV Charging Cybersecurity Under Aggressive and Stealth Cyberattacks"],"prefix":"10.3390","volume":"26","author":[{"given":"Marran","family":"Al Qwaid","sequence":"first","affiliation":[{"name":"Department of Computer Sciences, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4399-1417","authenticated-orcid":false,"given":"Gobbi","family":"Ramasamy","sequence":"additional","affiliation":[{"name":"Faculty of AI and Engineering, Multimedia University, Cyberjaya 63100, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3358-3197","authenticated-orcid":false,"given":"Md Sabbir","family":"Hossen","sequence":"additional","affiliation":[{"name":"Faculty of AI and Engineering, Multimedia University, Cyberjaya 63100, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/OJVT.2024.3358893","article-title":"Future Trends in Smart Green IoV: Vehicle-to-Everything in the Era of Electric Vehicles","volume":"5","author":"Aldhanhani","year":"2024","journal-title":"IEEE Open J. Veh. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"40672","DOI":"10.1109\/ACCESS.2026.3668809","article-title":"Wireless Power Transfer and IoT in EV Charging: Opportunities, Challenges, and Future Directions","volume":"14","author":"Karim","year":"2026","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108703","DOI":"10.1109\/ACCESS.2024.3437192","article-title":"On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case","volume":"12","author":"Dehrouyeh","year":"2024","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6695","DOI":"10.1109\/JSYST.2023.3305757","article-title":"Distributed Energy Resources Cybersecurity Outlook: Vulnerabilities, Attacks, Impacts, and Mitigations","volume":"17","author":"Zografopoulos","year":"2023","journal-title":"IEEE Syst. J."},{"key":"ref_5","first-page":"4231","article-title":"Advancing machine learning-driven cybersecurity solutions for secure electric vehicle charging in smart grids","volume":"17","author":"Poorani","year":"2025","journal-title":"Int. J. Inf. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1109\/OAJPE.2025.3562052","article-title":"Survey of Load-Altering Attacks Against Power Grids: Attack Impact, Detection, and Mitigation","volume":"12","author":"Maleki","year":"2025","journal-title":"IEEE Open Access J. Power Energy"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Elshazly, A.A., Elgarhy, I., Eltoukhy, A.T., Mahmoud, M., Eberle, W., Alsabaan, M., and Alshawi, T. (2024). False Data Injection Attacks on Reinforcement Learning-Based Charging Coordination in Smart Grids and a Countermeasure. Appl. Sci., 14.","DOI":"10.3390\/app142310874"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"10092","DOI":"10.1038\/s41598-025-92895-9","article-title":"Detection of cyber attacks in electric vehicle charging systems using a remaining useful life generative adversarial network","volume":"15","author":"Dinler","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hossen, M.S., Sarker, M.T., Nabi, M.S., Bannah, H., Ramasamy, G., and Eng Eng, N. (2025). Federated AI-OCPP Framework for Secure and Scalable EV Charging in Smart Cities. Urban Sci., 9.","DOI":"10.3390\/urbansci9090363"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"18530","DOI":"10.1109\/ACCESS.2026.3657040","article-title":"Real-World Tariff-Aware Safe Reinforcement Learning for Grid-Stable OCPP EV Charging Networks","volume":"14","author":"Hossen","year":"2026","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"36414","DOI":"10.1109\/ACCESS.2026.3668920","article-title":"Collaborative Optimization Scheduling Strategy of Electric Vehicle Load Cluster and Data Center for Multi-Station Integration","volume":"14","author":"Li","year":"2026","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111454","DOI":"10.1109\/ACCESS.2025.3580856","article-title":"Cyber Resilience of Electric Vehicle Charging in Smart Grids: The Dutch Case","volume":"13","author":"Hijgenaar","year":"2025","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5404","DOI":"10.1109\/ACCESS.2023.3349274","article-title":"A Comprehensive Review and Analysis of the Allocation of Electric Vehicle Charging Stations in Distribution Networks","volume":"12","author":"Yuvaraj","year":"2024","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"160767","DOI":"10.1109\/ACCESS.2025.3608856","article-title":"Comparative Analysis of Centralized and Federated Intrusion Detection in IoT-Enabled Cyber-Physical Systems Under Data and Label-Skew","volume":"13","author":"Rais","year":"2025","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"80637","DOI":"10.1109\/ACCESS.2025.3562128","article-title":"Recent Advances in Federated Learning for Connected Autonomous Vehicles: Addressing Privacy, Performance, and Scalability Challenges","volume":"13","author":"Ali","year":"2025","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3731","DOI":"10.1109\/TITS.2025.3579885","article-title":"Eliminate Conflicts and Attacks: Fair and Robust Federated Learning for Anomaly Detection of Charging Stations","volume":"27","author":"Liu","year":"2026","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"AlHousrya, O., Bennagi, A., Cotfas, P.A., and Cotfas, D.T. (2025). The Role of the Industrial IoT in Advancing Electric Vehicle Technology: A Review. Appl. Sci., 15.","DOI":"10.3390\/app15179290"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"18049","DOI":"10.1109\/TITS.2024.3408238","article-title":"Enhancing Cyber-Resilience in Electric Vehicle Charging Stations: A Multi-Agent Deep Reinforcement Learning Approach","volume":"25","author":"Sepehrzad","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"34896","DOI":"10.1109\/JIOT.2024.3447241","article-title":"A Comprehensive Review on Internet of Things Applications in Power Systems","volume":"11","author":"Mohanty","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6208","DOI":"10.1109\/JIOT.2024.3511961","article-title":"The Role of Generative Artificial Intelligence in Internet of Electric Vehicles","volume":"12","author":"Zhang","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hossen, M.S., Ramasamy, G., and Al Qwaid, M. (2026). An Integrated Machine Learning Framework for EV Charging Behavior Characterization and Anomaly Detection in Public Charging Infrastructure. Appl. Sci., 16.","DOI":"10.3390\/app16147203"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"215219","DOI":"10.1109\/ACCESS.2025.3645058","article-title":"Intrusion Detection Systems for In-Vehicle Networks: Protocols, Applications, and Challenges","volume":"13","author":"Khan","year":"2025","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3751","DOI":"10.1109\/COMST.2025.3570288","article-title":"Federated Learning for Cyber Physical Systems: A Comprehensive Survey","volume":"28","author":"Quan","year":"2026","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1109\/OJCS.2025.3590102","article-title":"Optimizing Federated Learning With Aggregation Strategies: A Comprehensive Survey","volume":"6","author":"Khan","year":"2025","journal-title":"IEEE Open J. Comput. Soc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"95410","DOI":"10.1109\/ACCESS.2025.3574672","article-title":"Systematic Analysis of Federated Learning Approaches for Intrusion Detection in the Internet of Things Environment","volume":"13","author":"Hamad","year":"2025","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1109\/OJCOMS.2025.3545153","article-title":"Cybersecurity Challenges in Smart Grid Systems: Current and Emerging Attacks, Opportunities, and Recommendations","volume":"6","author":"Amanlou","year":"2025","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101704","DOI":"10.1016\/j.segan.2025.101704","article-title":"Regression based anomaly detection in electric vehicle state of charge fluctuations through analysis of electric vehicle charging infrastructure data","volume":"42","author":"Mitikiri","year":"2025","journal-title":"Sustain. Energy Grids Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5182","DOI":"10.1109\/TSG.2024.3401090","article-title":"Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-Based Detection Mechanisms","volume":"15","author":"Jahangir","year":"2024","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hossen, M.S., Sarker, M.T., Al Qwaid, M., Ramasamy, G., and Eng Eng, N. (2025). AI-Driven Framework for Secure and Efficient Load Management in Multi-Station EV Charging Networks. World Electr. Veh. J., 16.","DOI":"10.3390\/wevj16070370"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hossen, M.S., Ramasamy, G., Eng Eng, N., and Qwaid, M.A. (2026). An Anomaly-Aware, Q-Learning Framework for Real-Time Scheduling in Multi-Station EV Charging Networks. Electronics, 15.","DOI":"10.3390\/electronics15112494"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, S., Liu, B., Li, Q., Han, D., Zhou, J., and Xiang, Y. (2025). EV Charging Behavior Analysis and Load Prediction via Order Data of Charging Stations. Sustainability, 17.","DOI":"10.3390\/su17051807"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"34755","DOI":"10.1109\/ACCESS.2026.3666978","article-title":"AI-OCPP Framework for Smart and Safe EV Charging: Real-World Performance Evaluation","volume":"14","author":"Hossen","year":"2026","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"131509","DOI":"10.1109\/ACCESS.2025.3590437","article-title":"Invisible Manipulation: Deep Reinforcement Learning-Enhanced Stealthy Attacks on Battery Energy Management Systems","volume":"13","author":"Xiao","year":"2025","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"134","DOI":"10.3390\/electricity4020009","article-title":"Data-Driven, Short-Term Prediction of Charging Station Occupation","volume":"4","author":"Aghsaee","year":"2023","journal-title":"Electricity"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1038\/s41467-025-67703-7","article-title":"Privacy-preserving collaborative battery fault warning for massive electric vehicles by heterogeneous data from charging stations","volume":"17","author":"Yang","year":"2026","journal-title":"Nat. Commun."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5541\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T04:13:03Z","timestamp":1788927183000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5541"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":35,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175541"],"URL":"https:\/\/doi.org\/10.3390\/s26175541","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]}}}