{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T09:06:45Z","timestamp":1789031205493,"version":"build-2803163510"},"reference-count":78,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"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>With the rapid proliferation of Internet of Things (IoT) technology today, the number of devices connected to each other through these networks is increasing significantly, and the security risks these devices face from cyberattacks have emerged as a clear problem. Intrusion detection systems (IDSs) are emerging as a solution to this problem and play a significant role in securing IoT-based networks. Although machine learning and deep learning-based IDS approaches have achieved high accuracy rates in detecting attacks in recent years, the lack of transparency in these models\u2019 decision-making processes poses a major drawback in terms of reliability and explainability. To ensure that the decisions of IDS models are understandable and to address this issue, Explainable AI (XAI) approaches are being implemented. This study provides a detailed review of the current literature on XAI-enabled IDSs developed for IoT environments. The studies examined are systematically evaluated in terms of the deep learning models used, lightweight model designs, explainability methods and validations, approaches to protecting data privacy, and datasets. The literature review highlights that research is not only focused on the accuracy of attack detection but also aims to design IDS solutions that are lightweight, explainable, and privacy-preserving. This paper aims to provide a foundational resource to guide future research on reliable, explainable, and practical IoT intrusion detection systems by identifying the problems addressed in current research and highlighting the limitations in the literature. In addition, this work proposes and applies a study assessment framework to systematically assess methodological adequacy, experimental reproducibility, lightweight deployment, XAI techniques, and XAI validation.<\/jats:p>","DOI":"10.3390\/s26185744","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T08:07:41Z","timestamp":1789027661000},"page":"5744","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable AI-Based Intrusion Detection Systems for IoT Environments: A Systematic Literature Review"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2679-4278","authenticated-orcid":false,"given":"Murat","family":"Varol","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Zonguldak B\u00fclent Ecevit University, Zonguldak 67100, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6970-3239","authenticated-orcid":false,"given":"Aykut","family":"Karakaya","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Zonguldak B\u00fclent Ecevit University, Zonguldak 67100, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s12559-026-10559-w","article-title":"XAI-driven Data Mining for Self-defending IoT Systems: Enhancing Cybersecurity Transparency in the Age of Smart Cities","volume":"18","author":"Khan","year":"2026","journal-title":"Cogn. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gupta, V.P. (2021). Smart sensors and industrial IoT (IIoT): A driver of the growth of industry 4.0. Smart Sensors for Industrial Internet of Things: Challenges, Solutions and Applications, Springer.","DOI":"10.1007\/978-3-030-52624-5_3"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"91357","DOI":"10.1109\/ACCESS.2024.3418995","article-title":"Internet of Things (IoT): Origins, embedded technologies, smart applications, and its growth in the last decade","volume":"12","author":"Yalli","year":"2024","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112410","DOI":"10.1016\/j.engappai.2025.112410","article-title":"An intrusion detection system for critical infrastructures: Modbus approach","volume":"162","author":"Varol","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3753","DOI":"10.1007\/s10586-022-03776-z","article-title":"Internet of Things intrusion detection systems: A comprehensive review and future directions","volume":"26","author":"Heidari","year":"2023","journal-title":"Clust. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jnca.2017.02.009","article-title":"A survey of intrusion detection in Internet of Things","volume":"84","author":"Miani","year":"2017","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Altulaihan, E., Almaiah, M.A., and Aljughaiman, A. (2024). Anomaly detection IDS for detecting DoS attacks in IoT networks based on machine learning algorithms. Sensors, 24.","DOI":"10.3390\/s24020713"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Arasteh, H., Hosseinnezhad, V., Loia, V., Tommasetti, A., Troisi, O., Shafie-Khah, M., and Siano, P. (2016). Iot-based smart cities: A survey. Proceedings of the 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), IEEE.","DOI":"10.1109\/EEEIC.2016.7555867"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3390\/smartcities4020024","article-title":"IoT in smart cities: A survey of technologies, practices and challenges","volume":"4","author":"Syed","year":"2021","journal-title":"Smart Cities"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6632599","DOI":"10.1155\/2021\/6632599","article-title":"IoT-based applications in healthcare devices","volume":"2021","author":"Pradhan","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s42452-019-1925-y","article-title":"Challenges and opportunities in IoT healthcare systems: A systematic review","volume":"2","author":"Selvaraj","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"127017","DOI":"10.1016\/j.neucom.2023.127017","article-title":"A review of IoT applications in healthcare","volume":"565","author":"Li","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Karacan, E., Akleylek, S., and Karakaya, A. (2021, January 28\u201329). PQ-FLAT: A New Quantum-Resistant And Lightweight Authentication Approach for M2M Devices. Proceedings of the 2021 9th International Symposium on Digital Forensics and Security (ISDFS), Elazig, Turkey.","DOI":"10.1109\/ISDFS52919.2021.9486341"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"69135","DOI":"10.1109\/ACCESS.2022.3186306","article-title":"Quantum Secure Communication Between Service Provider and Sim","volume":"10","author":"Karacan","year":"2022","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e1644","DOI":"10.1002\/wics.1644","article-title":"A survey on post-quantum based approaches for edge computing security","volume":"16","author":"Karakaya","year":"2024","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_16","first-page":"102118","article-title":"A low cost compact network TAP device with Raspberry Pi 4","volume":"70","author":"Varol","year":"2025","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Karakaya, A. (2025). A hybrid approach for IoT security: Combining ensemble learning with fuzzy logic. Sensors, 25.","DOI":"10.3390\/s25185668"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Andrade, R.O., Yoo, S.G., Ortiz-Garces, I., and Barriga, J. (2022). Security risk analysis in IoT systems through factor identification over IoT devices. Appl. Sci., 12.","DOI":"10.3390\/app12062976"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Alfahaid, A., Alalwany, E., Almars, A.M., Alharbi, F., Atlam, E., and Mahgoub, I. (2025). Machine learning-based security solutions for iot networks: A comprehensive survey. Sensors, 25.","DOI":"10.3390\/s25113341"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ngema, N., Nleya, B., and Maswanganyi, R.C. (2026). A Review of Zero Trust Architecture: Principles, Applications, and Implementation Challenges in Communication, Navigation, and Surveillance (CNS) Systems. Sensors, 26.","DOI":"10.3390\/s26123813"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Luo, Y.Y., Cheng, C.H., Lian, Y.R., Huang, Y.F., and Hsieh, C.H. (2026). An Integrated Zero-Trust and Real-Time Detection Scheme for DDoS Protection in 5G IoT Systems. Sensors, 26.","DOI":"10.3390\/s26113479"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Boukebous, A.A.E., Fettache, M.I., Bendiab, G., and Shiaeles, S. (2023). A comparative analysis of Snort 3 and Suricata. Proceedings of the 2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET), IEEE.","DOI":"10.1109\/GlobConET56651.2023.10150141"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1145\/3561048","article-title":"Explainable AI (XAI): Core ideas, techniques, and solutions","volume":"55","author":"Dwivedi","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s11416-022-00441-2","article-title":"XAI for intrusion detection system: Comparing explanations based on global and local scope","volume":"19","author":"Hariharan","year":"2023","journal-title":"J. Comput. Virol. Hacking Tech."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Arreche, O., Guntur, T., and Abdallah, M. (2024). Xai-ids: Toward proposing an explainable artificial intelligence framework for enhancing network intrusion detection systems. Appl. Sci., 14.","DOI":"10.3390\/app14104170"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alabbadi, A., and Bajaber, F. (2025). An intrusion detection system over the IoT data streams using eXplainable Artificial Intelligence (XAI). Sensors, 25.","DOI":"10.3390\/s25030847"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1007\/s11227-026-08494-6","article-title":"Unveiling intruders\u2019 behaviors: Explainable AI-based profiling of malicious bot activities in IoT networks","volume":"82","author":"Niktabe","year":"2026","journal-title":"J. Supercomput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5783","DOI":"10.1007\/s10115-025-02402-9","article-title":"Multi-attention DeepCRNN: An efficient and explainable intrusion detection framework for Internet of Medical Things environments","volume":"67","author":"Sharma","year":"2025","journal-title":"Knowl. Inf. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"12157","DOI":"10.1109\/TCE.2025.3601183","article-title":"Knowledge Distillation for Lightweight and Explainable Intrusion Detection in Resource-Constrained Consumer Devices","volume":"71","author":"Umair","year":"2025","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"109560","DOI":"10.1016\/j.engappai.2024.109560","article-title":"A deep learning ensemble approach for malware detection in Internet of Things utilizing Explainable Artificial Intelligence","volume":"139","author":"Mittal","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104417","DOI":"10.1016\/j.cose.2025.104417","article-title":"Transformer-based knowledge distillation for explainable intrusion detection system","volume":"154","author":"Nadiah","year":"2025","journal-title":"Comput. Secur."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"101722","DOI":"10.1016\/j.iot.2025.101722","article-title":"An explainable three-way neural network approach for intrusion detection in IoT ecosystem","volume":"33","author":"Wahab","year":"2025","journal-title":"Internet Things"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"12958","DOI":"10.1109\/JIOT.2026.3653260","article-title":"A Hybrid Explainable AI for DDoS Attacks Detection in Industrial IoT Networks","volume":"13","author":"Saiyed","year":"2026","journal-title":"IEEE Internet Things J."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Georgiades, M., and Hussain, F. (2025). An explainable ai approach for interpretable cross-layer intrusion detection in internet of medical things. Electronics, 14.","DOI":"10.3390\/electronics14163218"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101675","DOI":"10.1016\/j.iot.2025.101675","article-title":"XBiDeep: A novel explainable artificial intelligence based intrusion detection system for Internet of Medical Things environment","volume":"33","author":"Turgut","year":"2025","journal-title":"Internet Things"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1002\/spe.3319","article-title":"Blockchain and explainable AI for enhanced decision making in cyber threat detection","volume":"54","author":"Kumar","year":"2024","journal-title":"Softw. Pract. Exp."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"173368","DOI":"10.1109\/ACCESS.2025.3613459","article-title":"Federated Learning With Explainable AI for Malicious Traffic Detection in IoT Networks","volume":"13","author":"Iltaf","year":"2025","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"121751","DOI":"10.1016\/j.eswa.2023.121751","article-title":"Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach","volume":"238","author":"Sharma","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"192451","DOI":"10.1109\/ACCESS.2025.3630753","article-title":"A Hybrid Lightweight Deep Learning-Based Intrusion Detection Approach in IoT Utilizing Feature Selection & Explainable Artificial Intelligence","volume":"13","author":"Khan","year":"2025","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"101681","DOI":"10.1016\/j.iot.2025.101681","article-title":"An interpretable deep learning framework for intrusion detection in industrial Internet of Things","volume":"33","author":"Ahmad","year":"2025","journal-title":"Internet Things"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"109713","DOI":"10.1109\/ACCESS.2025.3582391","article-title":"StaEn-IDS: An Explainable Stacking Ensemble Deep Neural Network-Based Intrusion Detection System for IoT","volume":"13","author":"Vishwakarma","year":"2025","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"132398","DOI":"10.1016\/j.neucom.2025.132398","article-title":"DistilXIDS: Efficient, lightweight and explainable transformer-based language model for real-time network intrusion detection","volume":"668","author":"Ajayan","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"123853","DOI":"10.1016\/j.eswa.2024.123853","article-title":"An explainable nature-inspired cyber attack detection system in Software-Defined IoT applications","volume":"250","author":"Kumar","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"127368","DOI":"10.1109\/ACCESS.2025.3590159","article-title":"Bridging Explainability and Security: An XAI-Enhanced Hybrid Deep Learning Framework for IoT Device Identification and Attack Detection","volume":"13","author":"Jain","year":"2025","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"108270","DOI":"10.1016\/j.comcom.2025.108270","article-title":"Mitigating iot botnet attacks: An early-stage explainable network-based anomaly detection approach","volume":"241","author":"Korba","year":"2025","journal-title":"Comput. Commun."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.aej.2026.02.013","article-title":"DOAL-IDS: Deep Optimized Active Learning Framework for Intrusion Detection in IoT Systems","volume":"142","author":"Javed","year":"2026","journal-title":"Alex. Eng. J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"60792","DOI":"10.1109\/ACCESS.2026.3684727","article-title":"FCAST-RF: A Lightweight and Deeply Explainable Intrusion Detection Framework for Edge Gateways","volume":"14","author":"Zhang","year":"2026","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Alabbadi, A., and Bajaber, F. (2025). X-FuseRLSTM: A cross-domain explainable intrusion detection framework in IoT using the attention-guided dual-path feature fusion and residual LSTM. Sensors, 25.","DOI":"10.3390\/s25123693"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.aej.2024.09.115","article-title":"Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems","volume":"110","author":"Alotaibi","year":"2025","journal-title":"Alex. Eng. J."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"110561","DOI":"10.1016\/j.compeleceng.2025.110561","article-title":"A lightweight intrusion detection system using deep convolutional neural network","volume":"127","author":"Hnamte","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"115939","DOI":"10.1016\/j.chaos.2024.115939","article-title":"CPS-IoT-PPDNN: A new explainable privacy preserving DNN for resilient anomaly detection in Cyber-Physical Systems-enabled IoT networks","volume":"191","author":"Saheed","year":"2025","journal-title":"Chaos Solitons Fractals"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"112911","DOI":"10.1016\/j.engappai.2025.112911","article-title":"An explainable and adaptive Internet of Things intrusion detection system supported by Large Language Models","volume":"163","author":"Huang","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"113351","DOI":"10.1016\/j.knosys.2025.113351","article-title":"Explainable tabnet transformer-based on google vizier optimizer for anomaly intrusion detection system","volume":"316","author":"Fares","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"103937","DOI":"10.1016\/j.adhoc.2025.103937","article-title":"Real-time explainable IoT security with machine learning and CTGAN-enhanced detection for resource-constrained devices","volume":"178","author":"Hasan","year":"2025","journal-title":"Ad Hoc Netw."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"110143","DOI":"10.1016\/j.engappai.2025.110143","article-title":"Adaptable, incremental, and explainable network intrusion detection systems for internet of things","volume":"144","author":"Cerasuolo","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"111479","DOI":"10.1016\/j.comnet.2025.111479","article-title":"Federated Learning of Explainable AI (FEDXAI) for deep learning-based intrusion detection in IoT networks","volume":"270","author":"Kalakoti","year":"2025","journal-title":"Comput. Netw."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"104358","DOI":"10.1016\/j.jnca.2025.104358","article-title":"An intelligent and explainable intrusion detection framework for Internet of Sensor Things using generalizable optimized active Machine Learning","volume":"245","author":"Hasnain","year":"2026","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"42786","DOI":"10.1038\/s41598-025-27076-9","article-title":"Designing a neuro-symbolic dual-model architecture for explainable and resilient intrusion detection in IoT networks","volume":"15","author":"Almadhor","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1007\/s10586-024-05022-0","article-title":"mFCBF based lightweight intrusion detection system for IoT networks","volume":"28","author":"Kushwaha","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"6839","DOI":"10.1109\/TCE.2024.3482092","article-title":"Interactive and Explainable Optimized Learning for DDoS Detection in Consumer IoT Networks","volume":"71","author":"Saiyed","year":"2025","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"112936","DOI":"10.1016\/j.engappai.2025.112936","article-title":"Lightweight ensemble learning based intrusion detection framework with explainable artificial intelligence","volume":"163","author":"Gupta","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"36520","DOI":"10.1038\/s41598-025-10601-1","article-title":"Privacy preserving blockchain integrated explainable artificial intelligence with two tier optimization for cyber threat detection and mitigation in the internet of things","volume":"15","author":"Alohali","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1038\/s41598-025-31897-z","article-title":"A novel adaptive hybrid intrusion detection system with lightweight optimization for enhanced security in internet of medical things","volume":"16","author":"Saeed","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s10586-025-05836-6","article-title":"Enhancing IoMT security: A novel blockchain-based anomaly detection framework leveraging explainable AI","volume":"29","author":"Sharma","year":"2026","journal-title":"Clust. Comput."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"25454","DOI":"10.1109\/JIOT.2023.3327024","article-title":"An Intelligent and Explainable SaaS-Based Intrusion Detection System for Resource-Constrained IoMT","volume":"11","author":"Aljuhani","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1038\/s41598-025-31025-x","article-title":"FORT-IDS: A federated, optimized, robust and trustworthy intrusion detection system for IIoT security","volume":"16","author":"Mazroa","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1007\/s10586-025-05777-0","article-title":"OptiXID: An optimized and eXplainable AI framework for intrusion detection in IoT networks","volume":"28","author":"Albasheer","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"39937","DOI":"10.1038\/s41598-025-23750-0","article-title":"Interpretable intrusion detection for IoT environments using a self-attention-based explainable AI framework","volume":"15","author":"Sharma","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2313","DOI":"10.1038\/s41598-025-32183-8","article-title":"HED-ID: An edge-deployable and explainable intrusion detection system optimized via metaheuristic learning","volume":"16","author":"Nasir","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1007\/s10586-025-05617-1","article-title":"Explainable AI-driven intrusion detection for securing IoT-enabled autonomous transportation systems","volume":"28","author":"Akshya","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"111358","DOI":"10.1016\/j.engappai.2025.111358","article-title":"A convolutional neural network-enhanced attack detection framework with explainable artificial intelligence for internet of things-based metaverse security","volume":"158","author":"Jokic","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"104542","DOI":"10.1016\/j.cose.2025.104542","article-title":"Explainable AI and random forest based reliable intrusion detection system","volume":"157","author":"Wali","year":"2025","journal-title":"Comput. Secur."},{"key":"ref_73","first-page":"104301","article-title":"A multilayered deep learning framework for cyber attack detection and mitigation in a heterogeneous IIoT ecosystem","volume":"96","author":"Iqbal","year":"2026","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_74","first-page":"2573","article-title":"An Impact-Aware and Taxonomy-Driven Explainable Machine Learning Framework with Edge Computing for Security in Industrial IoT\u2013Cyber Physical Systems","volume":"145","author":"Zhukabayeva","year":"2025","journal-title":"Comput. Model. Eng. Sci."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Kanipakam, S., Devarajulu, V.S., and Kavarakuntla, T. (2025). Privacy-Preserving AI Inference in Edge Systems: Ethical and Architectural Tradeoffs. Proceedings of the 2025 IEEE International Performance, Computing, and Communications Conference (IPCCC), IEEE.","DOI":"10.1109\/IPCCC66453.2025.11304649"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Nadeem, A., Vos, D., Cao, C., Pajola, L., Dieck, S., Baumgartner, R., and Verwer, S. (2023, January 3\u20137). SoK: Explainable Machine Learning for Computer Security Applications. Proceedings of the 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P), Delft, The Netherlands.","DOI":"10.1109\/EuroSP57164.2023.00022"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Xue, Q., Xue, P., Wang, Z., and Ma, H. (2026). Artificial Intelligence for Cybersecurity in IoT-Edge Systems: A Structured Review of Methods, Datasets, Evaluation, and Deployment Challenges. Electronics, 15.","DOI":"10.3390\/electronics15112409"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1145\/3675401","article-title":"Hybrid Prompt Learning for Generating Justifications of Security Risks in Automation Rules","volume":"15","author":"Breve","year":"2024","journal-title":"ACM Trans. Intell. Syst. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5744\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T08:10:38Z","timestamp":1789027838000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5744"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,10]]},"references-count":78,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185744"],"URL":"https:\/\/doi.org\/10.3390\/s26185744","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,10]]}}}