{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T17:29:43Z","timestamp":1786469383069,"version":"build-2736575974"},"reference-count":88,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006701","name":"Umm al-Qura University","doi-asserted-by":"publisher","award":["26UQU44680374GSSR01"],"award-info":[{"award-number":["26UQU44680374GSSR01"]}],"id":[{"id":"10.13039\/501100006701","id-type":"DOI","asserted-by":"publisher"}]},{"award":["26UQU44680374GSSR01"],"award-info":[{"award-number":["26UQU44680374GSSR01"]}],"id":[{"id":"https:\/\/ror.org\/01xjqrm90","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Modern Industrial Internet of Things (IIoT) integrates machine learning models for monitoring and control. However, they remain vulnerable to adversarial machine learning (AML) attacks, where an adversary adds small changes to the input data. These small changes degrade model quality, reduce accuracy, and can ultimately compromise the safety and security of the entire system. AML research in IIoT often focuses on individual attack types, defense methods, and datasets. Existing reviews lack a unified quantitative and system-level perspective. Therefore, a systematic literature review (SLR) is needed to provide a holistic analysis of existing attacks, defenses, and databases. This SLR analyzes 50 research articles to provide a holistic view of AML threats in IIoT systems and identifies seven distinct attack types: gradient-based perturbations, GAN-generated samples, poisoning attacks, reinforcement learning-based (RL) strategies, saliency-based feature manipulation, false data injection, and hybrid approaches. To illustrate the range of observed impacts, selected studies report the following degradation examples: saliency-based attacks cause accuracy reductions of 6\u201311 percentage points; iterative gradient attacks reduce accuracy from 95\u201399% to 30\u201340% in SIEM systems; and RL-based attacks reduce detection rates from 100% to 0% in rule-based IDS settings. In addition to the analysis of attack types, this SLR also evaluates current defense methods to protect IIoT systems. It has been observed that existing defense mechanisms lack generalization and require high computational resources. Moreover, the testing is performed under simplified threat models. The analysis of datasets further shows a clear gap between realistic industrial benchmarks (such as SWaT, WADI, and NSL-KDD) and synthetic datasets used for controlled experiments. By connecting attack behavior, defense performance, dataset characteristics, and system-level effects, this SLR identifies the key research gaps that must be addressed in future work.<\/jats:p>","DOI":"10.3390\/s26165098","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T16:30:32Z","timestamp":1786465832000},"page":"5098","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adversarial Machine Learning in Industrial IoT: A Systematic Review of Attack Realism, Defense Trade-Offs, and Deployment Gaps"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2673-1935","authenticated-orcid":false,"given":"Abeer","family":"Alsaidlani","sequence":"first","affiliation":[{"name":"Department of Computer and Networks Engineering, College of Computing, Umm Al-Qura University, Makkah 24382, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5852-1296","authenticated-orcid":false,"given":"Muhammad","family":"Rashid","sequence":"additional","affiliation":[{"name":"Department of Computer and Networks Engineering, College of Computing, Umm Al-Qura University, Makkah 24382, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9255-6094","authenticated-orcid":false,"given":"Malak","family":"Aljabri","sequence":"additional","affiliation":[{"name":"Department of Computer and Networks Engineering, College of Computing, Umm Al-Qura University, Makkah 24382, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,11]]},"reference":[{"key":"ref_1","first-page":"0011","article-title":"Security and Privacy in the IIoT: Threats, Possible Security Countermeasures, and Future Challenges","volume":"2","year":"2025","journal-title":"Comput. AI Connect"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dhirani, L.L., Armstrong, E., and Newe, T. (2021). Industrial IoT, Cyber Threats, and Standards Landscape: Evaluation and Roadmap. Sensors, 21.","DOI":"10.3390\/s21113901"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1109\/JIOT.2024.3465549","article-title":"Universal Adversarial Perturbations Against Machine-Learning-Based Intrusion Detection Systems in Industrial IoT","volume":"12","author":"Zhang","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4878","DOI":"10.1109\/TCE.2024.3383608","article-title":"Guest Editorial: Data-Driven Innovation and Adversarial Learning Models for Industry 5.0 Toward Consumer Digital Ecosystems","volume":"70","author":"Sangaiah","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12071","DOI":"10.1007\/s13369-024-09663-6","article-title":"Integrated Genetic Algorithm and Deep Learning Approach for Effective Cyber-Attack Detection and Classification in Industrial Internet of Things (IIoT) Environments","volume":"50","author":"Alkhafaji","year":"2024","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhukabayeva, T., Zholshiyeva, L., Karabayev, N., Khan, S., and Alnazzawi, N. (2025). Cybersecurity Solutions for Industrial Internet of Things\u2013Edge Computing Integration: Challenges, Threats, and Future Directions. Sensors, 25.","DOI":"10.3390\/s25010213"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Leander, B., \u010cau\u0161evi\u0107, A., and Hansson, H. (2019, January 10\u201313). Cybersecurity Challenges in Large Industrial IoT Systems. Proceedings of the 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Zaragoza, Spain.","DOI":"10.1109\/ETFA.2019.8869162"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alnajim, A., Habib, S., Islam, M., Thwin, S., and Alotaibi, F. (2023). A Comprehensive Survey of Cybersecurity Threats, Attacks, and Effective Countermeasures in Industrial Internet of Things. Technologies, 11.","DOI":"10.3390\/technologies11060161"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5728","DOI":"10.1109\/TII.2022.3155656","article-title":"Deep Generative Models in the Industrial Internet of Things: A Survey","volume":"18","author":"De","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5655","DOI":"10.1109\/TII.2025.3556075","article-title":"Generating Adversarial Malware Examples Against Multiple Machine Learning Detectors","volume":"21","author":"Sang","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lalos, A.S., Kalogeras, A.P., Koulamas, C., Tselios, C., Alexakos, C., and Serpanos, D. (2019). Secure and Safe IIoT Systems via Machine and Deep Learning Approaches. Security and Quality in Cyber-Physical Systems Engineering, Springer. Lecture Notes in Networks and Systems.","DOI":"10.1007\/978-3-030-25312-7_16"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mehmood, M., Baig, Z., and Syed, N. (2025). Adversarial Machine Learning Techniques for the Industrial IoT Paradigm. Advanced Machine Learning for Cyber-Attack Detection in IoT Networks, Academic Press.","DOI":"10.1016\/B978-0-44-329032-9.00008-7"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Raimundo, R.J., and Ros\u00e1rio, A.T. (2022). Cybersecurity in the Internet of Things in Industrial Management. Appl. Sci., 12.","DOI":"10.3390\/app12031598"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109299","DOI":"10.1016\/j.ress.2023.109299","article-title":"Transfer Adversarial Attacks Across Industrial Intelligent Systems","volume":"237","author":"Yin","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8618","DOI":"10.1109\/JIOT.2022.3222439","article-title":"Robust Monitor for Industrial IoT Condition Prediction","volume":"10","author":"Zhang","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111169","DOI":"10.1016\/j.asoc.2023.111169","article-title":"A Locational False Data Injection Attack Detection Method in Smart Grid Based on Adversarial Variational Autoencoders","volume":"151","author":"Wang","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.comcom.2020.12.013","article-title":"On Deep Reinforcement Learning Security for Industrial Internet of Things","volume":"168","author":"Liu","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1109\/TIFS.2019.2922398","article-title":"ADVoIP: Adversarial Detection of Encrypted and Concealed VoIP","volume":"15","author":"Addesso","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2086","DOI":"10.1109\/TNSE.2023.3240687","article-title":"A Stackelberg Game Approach to the Stability of Networked Switched Systems Under DoS Attacks","volume":"10","author":"Lian","year":"2023","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/TIFS.2022.3229595","article-title":"A Game-Theoretic Method for Defending Against Advanced Persistent Threats in Cyber Systems","volume":"18","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8442","DOI":"10.1109\/TII.2020.3043458","article-title":"Fed-IIoT: A Robust Federated Malware Detection Architecture in Industrial IoT","volume":"17","author":"Taheri","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"163","DOI":"10.3390\/iot2010009","article-title":"Cyber Threats to Industrial IoT: A Survey on Attacks and Countermeasures","volume":"2","author":"Tsiknas","year":"2021","journal-title":"IoT"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"11179","DOI":"10.1109\/JIOT.2021.3126633","article-title":"Deep Learning for Securing Software-Defined Industrial Internet of Things: Attacks and Countermeasures","volume":"9","author":"Wang","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2248","DOI":"10.1109\/COMST.2021.3094360","article-title":"A Survey on Industrial Control System Testbeds and Datasets for Security Research","volume":"23","author":"Conti","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103988","DOI":"10.1016\/j.cose.2024.103988","article-title":"Adversarial Machine Learning in Industry: A Systematic Literature Review","volume":"145","author":"Jedrzejewski","year":"2024","journal-title":"Comput. Secur."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jadidi, Z., Pal, S., Nayak, N.K., Selvakkumar, A., Chang, C.-C., Beheshti, M., and Jolfaei, A. (2022). Security of Machine Learning-Based Anomaly Detection in Cyber-Physical Systems. arXiv.","DOI":"10.1109\/ICCCN54977.2022.9868845"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1145\/3453158","article-title":"Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain","volume":"54","author":"Rosenberg","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1007\/s10462-025-11147-4","article-title":"Adversarial Machine Learning: A Review of Methods, Tools, and Critical Industry Sectors","volume":"58","author":"Pelekis","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Khazane, H., Ridouani, M., Salahdine, F., and Kaabouch, N. (2024). A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks. Future Internet, 16.","DOI":"10.3390\/fi16010032"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jahan, T. (2023, January 8\u20139). A Review of Adversarial Machine Learning for Cyber Security in Intelligent Systems Using Image and Signal Classification. Proceedings of the IEEE International Conference on ICT in Business Industry & Government (ICTBIG), Indore, India.","DOI":"10.1109\/ICTBIG59752.2023.10456232"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/s10462-024-11005-9","article-title":"Robustness in Deep Learning Models for Medical Diagnostics: Security and Adversarial Challenges Towards Robust AI Applications","volume":"58","author":"Javed","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Siva Kumar, R.S., Nystr\u00f6m, M., Lambert, J., Marshall, A., Goertzel, M., and Comissoneru, A. (2020, January 21). Adversarial Machine Learning-Industry Perspectives. Proceedings of the IEEE Security and Privacy Workshops (SPW), San Francisco, CA, USA.","DOI":"10.1109\/SPW50608.2020.00028"},{"key":"ref_33","unstructured":"Wu, K., Wang, L., and Chen, Y. (2022). A Survey of Adversarial Attacks on Wireless Communications. Edge Computing and IoT: Systems, Management and Security. ICECI 2021, Springer. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1109\/COMST.2021.3086014","article-title":"Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications","volume":"23","author":"Hu","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3014","DOI":"10.1109\/COMST.2023.3326399","article-title":"Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues","volume":"25","author":"Akrout","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1007\/s11277-025-11770-y","article-title":"Machine Learning in Future Wireless Communications: Innovations, Applications, and Open Challenges","volume":"140","author":"Fatima","year":"2025","journal-title":"Wirel. Pers. Commun."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"22265","DOI":"10.1007\/s11356-021-13178-0","article-title":"Guidelines for Developing a Systematic Literature Review for Studies Related to Climate Change Adaptation","volume":"28","author":"Shaffril","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_38","first-page":"13","article-title":"A Literature Review on Model Conversion, Inference, and Learning Strategies in EdgeML with TinyML Deployment","volume":"83","author":"Arif","year":"2025","journal-title":"Comput. Mater. Contin."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Alharthi, S., Rashid, M., and Aljabri, M. (2026). TinyML in Industrial IoT: A Systematic Review of Applications, System Components, and Methodologies. Sensors, 26.","DOI":"10.3390\/s26082550"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tasadduq, I.A., and Rashid, M. (2025). Toward Intelligent Underwater Acoustic Systems: Systematic Insights into Channel Estimation and Modulation Methods. Electronics, 14.","DOI":"10.20944\/preprints202507.0077.v1"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sonbul, O.S., and Rashid, M. (2025). Bridge Structural Health Monitoring: A Multi-Dimensional Taxonomy and Evaluation of Anomaly Detection Methods. Buildings, 15.","DOI":"10.20944\/preprints202507.1756.v1"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sonbul, O., and Rashid, M. (2023). Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review. Sensors, 23.","DOI":"10.3390\/s23094230"},{"key":"ref_43","first-page":"103647","article-title":"VAASI: Crafting Valid and Abnormal Adversarial Samples for Anomaly Detection Systems in Industrial Scenarios","volume":"79","year":"2023","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"102901","DOI":"10.1016\/j.cose.2022.102901","article-title":"Practical Evaluation of Poisoning Attacks on Online Anomaly Detectors in Industrial Control Systems","volume":"122","author":"Kravchik","year":"2022","journal-title":"Comput. Secur."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.procs.2021.03.072","article-title":"Crafting Adversarial Samples for Anomaly Detectors in Industrial Control Systems","volume":"184","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.procs.2022.01.240","article-title":"Generative Adversarial Networks with Bio-Inspired Primary Visual Cortex for Industry 4.0","volume":"200","author":"Branytskyi","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"102661","DOI":"10.1016\/j.adhoc.2021.102661","article-title":"Deep Learning-Based Reliable Routing Attack Detection Mechanism for Industrial Internet of Things","volume":"123","author":"Nayak","year":"2021","journal-title":"Ad Hoc Netw."},{"key":"ref_48","first-page":"102717","article-title":"Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems","volume":"58","author":"Anthi","year":"2021","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Mustafa, A., Khan, M.T., Umer, M.A., and Ahmed, C.M. (2025, January 6\u20139). Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems. Proceedings of the 1st Workshop on Modeling and Verification for Secure and Performant Cyber-Physical Systems (MoVe4SPS), Irvine, CA, USA.","DOI":"10.1145\/3735948.3736158"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Gungor, O., Rosing, T., and Aksanli, B. (2023, January 9\u201312). Adversarial-HD: Hyperdimensional Computing Adversarial Attack Design for Secure Industrial Internet of Things. Proceedings of the Cyber-Physical Systems and Internet of Things Week (CPS-IoT Week), San Antonio, TX, USA.","DOI":"10.1145\/3576914.3587484"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Tu, Y., Rampazzi, S., and Hei, X. (2023, January 26). Towards Adversarial Process Control on Inertial Sensor Systems with Physical Feedback Side Channels. Proceedings of the CPS&IoT Security and Privacy Workshop (CPSIoTSec), Copenhagen, Denmark.","DOI":"10.1145\/3605758.3623494"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Woo, S.S., Yoon, D., Gim, Y., and Park, E. (2024, January 8\u201312). RAAD: Reinforced Adversarial Anomaly Detector. Proceedings of the 39th ACM\/SIGAPP Symposium on Applied Computing (SAC), Avila, Spain.","DOI":"10.1145\/3605098.3635920"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Trivedi, S., Tran, T.A., Faruqui, N., and Hassan, M.M. (2023, January 5\u20136). An Exploratory Analysis of Effect of Adversarial Machine Learning Attack on IoT-enabled Industrial Control Systems. Proceedings of the International Conference on Smart Computing and Application (ICSCA), Hail, Saudi Arabia.","DOI":"10.1109\/ICSCA57840.2023.10087713"},{"key":"ref_54","first-page":"4415","article-title":"Robust Malware Defense in Industrial IoT Applications Using Machine Learning with Selective Adversarial Samples","volume":"56","author":"Khoda","year":"2020","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Agbedanu, P.R., Yang, S.J., Musabe, R., Gatare, I., and Rwigema, J. (2024, January 8\u201312). An Online Adaptive Approach to Detecting Zero-day Attacks in IoT and IIoT Systems. Proceedings of the IEEE GLOBECOM Workshops, Cape Town, South Africa.","DOI":"10.1109\/GCWkshp64532.2024.11101063"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"29540","DOI":"10.1109\/JIOT.2025.3568503","article-title":"Modeling Realistic Adversarial Traffic Against Deep-Learning-Based Intrusion Detection System in Industrial IoT","volume":"12","author":"Yao","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"6247","DOI":"10.1109\/JIOT.2020.3024800","article-title":"Intrusion Detection for Cyber\u2013Physical Systems Using Generative Adversarial Networks in Fog Environment","volume":"8","author":"Kaddoum","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Madupati, B., Somayajula, R., Vegesna, R.V., and Dasi, U. (2025, January 9\u201311). Edge-Aware Federated Learning: A Scalable and Fault-Tolerant System Architecture. Proceedings of the Global Conference in Emerging Technology (GINOTECH), Pune, India.","DOI":"10.1109\/GINOTECH63460.2025.11077045"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"19147","DOI":"10.1109\/JIOT.2022.3163894","article-title":"Toward Generative Adversarial Networks for the Industrial Internet of Things","volume":"9","author":"Qian","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1109\/TII.2022.3170149","article-title":"Distribution Bias Aware Collaborative Generative Adversarial Network for Imbalanced Deep Learning in Industrial IoT","volume":"19","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1109\/TII.2022.3165890","article-title":"SIEMS: A Secure Intelligent Energy Management System for Industrial IoT Applications","volume":"19","author":"Asef","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2832","DOI":"10.1109\/JIOT.2022.3194703","article-title":"Adversarial Attacks Against Acoustic Monitoring of Industrial Machines","volume":"10","author":"Ntalampiras","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kim, M.-S. (2024). Deep Reinforcement Learning-Based Adversarial Attack and Defense in Industrial Control Systems. Mathematics, 12.","DOI":"10.3390\/math12243900"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s11265-025-01962-x","article-title":"Adversarial Data Augmentation Enhanced LSTM Autoencoder for Anomaly Detection in Industrial Pipeline Networks","volume":"97","author":"Chen","year":"2025","journal-title":"J. Signal Process. Syst."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"15723","DOI":"10.1109\/JIOT.2023.3264492","article-title":"Cybersecurity Analysis of Data-Driven Power System Stability Assessment","volume":"10","author":"Zhang","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1109\/TDSC.2020.3037500","article-title":"Generating Adversarial Examples Against Machine Learning-Based Intrusion Detector in Industrial Control Systems","volume":"19","author":"Chen","year":"2022","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"102727","DOI":"10.1016\/j.adhoc.2021.102727","article-title":"A Collaborative Deep Learning Microservice for Backdoor Defenses in Industrial IoT Networks","volume":"124","author":"Liu","year":"2022","journal-title":"Ad Hoc Netw."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e13520","DOI":"10.1016\/j.heliyon.2023.e13520","article-title":"EIFDAA: Evaluation of an IDS with Function-Discarding Adversarial Attacks in the IIoT","volume":"9","author":"Li","year":"2023","journal-title":"Heliyon"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"111080","DOI":"10.1016\/j.compeleceng.2026.111080","article-title":"XAI-Enhanced adversarial resilient deep learning framework for transparent and secure edge deployment in consumer Internet of Things\/Industrial Internet of Things environments","volume":"133","author":"Saheed","year":"2026","journal-title":"Comput. Electr. Eng."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"101905","DOI":"10.1016\/j.iot.2026.101905","article-title":"Enhancing healthcare security: Manifold-aware machine learning for robust adversarial attack detection in IoMT networks","volume":"37","author":"Kaosar","year":"2026","journal-title":"Internet Things"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Bhattacharjee, S., Islam, M.J., and Abedzadeh, S. (2022, January 30). Robust Anomaly-Based Attack Detection in Smart Grids under Data Poisoning Attacks. Proceedings of the ACM Cyber-Physical System Security Workshop (CPSS), Nagasaki, Japan.","DOI":"10.1145\/3494107.3522778"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1145\/3596222","article-title":"Automated Adversary-in-the-Loop Cyber-Physical Defense Planning","volume":"7","author":"Banik","year":"2023","journal-title":"ACM Trans. Cyber-Phys. Syst."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Sharmin, Z., Xiang, Y., Uddin, M.P., and Chen, F. (2026). AdaFed-HybridGAN: Adaptive Federated Aggregation with GAN-Driven Hybrid Model\u2013Data Synthesis for Efficient Cyber Attack Detection in Edge Computing, Association for Computing Machinery.","DOI":"10.1145\/3793638.3793644"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.1109\/TIA.2023.3297089","article-title":"Design of Reliable IoT Systems With Deep Learning to Support Resilient Demand Side Management in Smart Grids Against Adversarial Attacks","volume":"60","author":"Elsisi","year":"2024","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Shepherd, P., Dagiuklas, T., Alkan, B., and Rodriguez, J. (2025, January 8\u201312). Dynamic Trust Management for Secure Federated Learning in Critical Industrial and IoT Networks. Proceedings of the IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada.","DOI":"10.1109\/ICCWorkshops67674.2025.11162455"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/TCE.2025.3531045","article-title":"Trust Score Prediction and Management in IoT Ecosystems Using Markov Chains and MADM Techniques","volume":"71","author":"Bampatsikos","year":"2025","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"6522","DOI":"10.1109\/TII.2019.2931436","article-title":"Physical Layer Security for the Smart Grid: Vulnerabilities, Threats, and Countermeasures","volume":"15","author":"Islam","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"74000","DOI":"10.1109\/ACCESS.2023.3291599","article-title":"APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System","volume":"11","author":"Javed","year":"2023","journal-title":"IEEE Access"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2705","DOI":"10.1109\/ACCESS.2024.3524996","article-title":"Detection of Adversarial Attacks Using Deep Learning and Features Extracted From Interpretability Methods in Industrial Scenarios","volume":"13","year":"2025","journal-title":"IEEE Access"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"6092","DOI":"10.1109\/TII.2020.2974555","article-title":"A Trustworthy Privacy-Preserving Framework for Machine Learning in Industrial IoT Systems","volume":"16","author":"Arachchige","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/TII.2022.3156642","article-title":"Federated Semi-Supervised Learning for Attack Detection in Industrial Internet of Things","volume":"19","author":"Aouedi","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1109\/TII.2022.3189046","article-title":"Mitigating Malicious Adversaries Evasion Attacks in Industrial Internet of Things","volume":"19","author":"Rafiq","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Benaddi, H., Jouhari, M., Ibrahimi, K., Ben Othman, J., and Amhoud, E.M. (2022). Anomaly Detection in Industrial IoT Using Distributional Reinforcement Learning and Generative Adversarial Networks. Sensors, 22.","DOI":"10.3390\/s22218085"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s12083-024-01838-0","article-title":"Strengthening Security in IoT-Based Smart Cities Utilizing Cycle-Consistent Generative Adversarial Networks for Attack Detection and Secure Data Transmission","volume":"18","author":"Agitha","year":"2025","journal-title":"Peer-to-Peer Netw. Appl."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Wankhade, K.K., Dongre, S., Chandra, R., Krishnan, K.V., and Arasavilli, S. (2024). Machine Learning-Based Detection of Attacks and Anomalies in Industrial Internet of Things (IIoT) Networks. Applied Soft Computing and Communication Networks, Springer. Lecture Notes in Networks and Systems.","DOI":"10.1007\/978-981-97-2004-0_7"},{"key":"ref_86","first-page":"79","article-title":"Abnormal Traffic Detection Based on Memory-Augmented Generative Adversarial IIoT-Assisted Network","volume":"18","author":"Wang","year":"2022","journal-title":"Wirel. Netw."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"4107","DOI":"10.1038\/s41598-025-11883-1","article-title":"SecuFL-IoT: An adaptive privacy-preserving federated learning framework for anomaly detection in smart industrial networks","volume":"16","author":"Alqazzaz","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"104403","DOI":"10.1016\/j.cose.2025.104403","article-title":"RADIANT: Reactive Autoencoder Defense for Industrial Adversarial Network Threats","volume":"154","author":"Khan","year":"2025","journal-title":"Comput. Secur."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5098\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T16:31:19Z","timestamp":1786465879000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5098"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,11]]},"references-count":88,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26165098"],"URL":"https:\/\/doi.org\/10.3390\/s26165098","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,11]]}}}