{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T04:30:32Z","timestamp":1786681832504,"version":"build-2736575974"},"reference-count":74,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"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) devices across smart cities, healthcare systems, industrial automation, transportation networks, smart grids, and cyber-physical infrastructures has expanded the modern cyberattack surface. IoT devices are often constrained by limited processing capacity, memory, battery power, and communication bandwidth, making conventional security mechanisms difficult to deploy consistently at scale. Intrusion detection systems (IDSs) provide an important defensive layer; however, many machine-learning-based IDSs are developed under static assumptions and may experience performance degradation as traffic distributions evolve due to firmware changes, device onboarding, protocol updates, user behavior variation, or adaptive attacks. This paper presents a hybrid IDS framework that integrates supervised Random Forest classification, unsupervised Isolation Forest anomaly monitoring, and Kolmogorov\u2013Smirnov (KS)-based concept drift monitoring. In the experimental pipeline, Isolation Forest is trained exclusively on benign traffic to ensure that the anomaly detector models normal behavior rather than an attack-dominated training distribution. The evaluation uses a large-scale chronologically sampled subset of the CICIoT2023 dataset containing 3,890,621 records while preserving the natural class distribution of 2.35% benign traffic and 97.65% attack traffic. The chronological 80\/20 train\/test split is established first at the file level, followed by systematic sampling within each split to reduce the risk of leakage across the evaluation boundary. On the 746,094-record test set, the proposed hybrid IDS achieved 99.73% accuracy, 99.89% precision, 99.83% recall, 99.86% F1-score, and a false positive rate of 4.77%. The corresponding confusion matrix contains TN = 16,683, FP = 836, FN = 1205, and TP = 727,370, yielding 95.23% specificity and 97.53% balanced accuracy. Standalone Random Forest marginally outperformed the hybrid model in raw accuracy and false positive rate; therefore, the contribution of the proposed framework is centered on deployment-oriented anomaly monitoring, drift awareness, and generalization rather than absolute superiority in static classification metrics. A leave-one-attack-family-out experiment withholding MITM-ArpSpoofing from training showed that the hybrid model detected 85.26% of the unseen attack-family samples, compared with 85.18% for Random Forest alone and 7.05% for Isolation Forest alone. These findings provide initial evidence of generalization to one held-out attack family but should not be interpreted as proof of broad zero-day detection capability. The framework is therefore positioned as a competitive IDS that combines supervised detection with anomaly monitoring and concept drift awareness for deployment-oriented IoT security.<\/jats:p>","DOI":"10.3390\/s26165117","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T14:45:06Z","timestamp":1786545906000},"page":"5117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Intrusion Detection System with Real-Time Concept Drift Detection for Enhanced IoT Security"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2963-7315","authenticated-orcid":false,"given":"Muath A.","family":"Obaidat","sequence":"first","affiliation":[{"name":"John Jay College of Criminal Justice, The City University of New York, New York, NY 10019, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meryem","family":"Abouali","sequence":"additional","affiliation":[{"name":"John Jay College of Criminal Justice, The City University of New York, New York, NY 10019, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aneeza","family":"Shakeel","sequence":"additional","affiliation":[{"name":"Department of Cybersecurity, Tandon School of Engineering, New York University (NYU), New York, NY 10012, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, S., Zheng, X., Li, J., Xu, J., and Ngai, E.C.H. (2026, June 10). CoLD: Collaborative Label Denoising Framework for Network Intrusion Detection. NDSS Symposium. Available online: https:\/\/www.ndss-symposium.org\/wp-content\/uploads\/2026-s1950-paper.pdf.","DOI":"10.14722\/ndss.2026.231950"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kumar, S., Munuswamy, S., and Arputharaj, K. (2023). A Comprehensive Survey on Machine Learning-Based Intrusion Detection Systems for Secure Communication in Internet of Things. Comput. Intell. Neurosci., 2023.","DOI":"10.1155\/2023\/8981988"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1109\/COMST.2020.2988293","article-title":"A Survey of Machine and Deep Learning Methods for Internet of Things Security","volume":"22","author":"Mohamed","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_4","unstructured":"Hajj, S., Azar, J., Bou Abdo, J., Demerjian, J., Guyeux, C., Makhoul, A., and Ginhac, D. (2026, February 28). Lightweight IoT Intrusion Detection Systems. Encyclopedia. Available online: https:\/\/encyclopedia.pub\/entry\/48571."},{"key":"ref_5","first-page":"63","article-title":"AI-Driven Cybersecurity for IoT-Cloud Ecosystems","volume":"3","author":"Zaka","year":"2025","journal-title":"Phys. Educ. Health Soc. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Doshi, R., Apthorpe, N., and Feamster, N. (2018). Machine Learning DDoS Detection for Consumer Internet of Things Devices. IEEE Security and Privacy Workshops (SPW), IEEE.","DOI":"10.1109\/SPW.2018.00013"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Garg, U., Mishra, P., Gupta, N., and Pilli, E.S. (2025). IoT Botnets Unveiled: Architectural Analysis, Threat Vectors, and Cutting-Edge Detection Techniques. Clust. Comput., 28.","DOI":"10.1007\/s10586-025-05633-1"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mahanipour, A., and Khamfroush, H. (2024). Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems. arXiv.","DOI":"10.1109\/DCOSS-IoT61029.2024.00086"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"24520","DOI":"10.1007\/s11227-024-06345-w","article-title":"A Network Intrusion Detection System Based on Deep Learning in the IoT","volume":"80","author":"Wang","year":"2024","journal-title":"J. Supercomput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ishtiaq, W., Zannat, A., Shahariar Parvez, A., Alamgir Hossain, M., Hasan Kanchan, M., and Masud Tarek, M. (2025). CST-AFNet: A Dual Attention-Based Deep Learning Framework for Intrusion Detection in IoT Networks. arXiv.","DOI":"10.1016\/j.array.2025.100501"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","article-title":"A Deep Learning Approach to Network Intrusion Detection","volume":"2","author":"Shone","year":"2018","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_13","first-page":"210","article-title":"A Lightweight Optimized Deep Learning-Based Host-Intrusion Detection System Deployed on the Edge for IoT","volume":"11","author":"Idrissi","year":"2022","journal-title":"Int. J. Comput. Digit. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3294","DOI":"10.1016\/j.procs.2023.10.323","article-title":"A New Concept Drift Detection Method Based on the Ranking of Features in a Data Stream","volume":"225","author":"Wrobel","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Arora, S., Rani, R., and Saxena, N. (2024). A Systematic Review on Detection and Adaptation of Concept Drift in Streaming Data Using Machine Learning Techniques. Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 14.","DOI":"10.1002\/widm.1536"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, J., Malialis, K., and Polycarpou, M.M. (2024). Concept Drift Adaptation by Exploiting Drift Type. ACM Trans. Knowl. Discov. From Data, 18.","DOI":"10.1145\/3638777"},{"key":"ref_17","first-page":"278","article-title":"Adaptive Hybrid Deep Learning Model for Real-Time Anomaly Detection in IoT Networks","volume":"137","author":"Alomari","year":"2025","journal-title":"J. Adv. Res. Des."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Altunay, H.C., and Albayrak, Z. (2023). A Hybrid CNN+LSTM-Based Intrusion Detection System for Industrial IoT Networks. Eng. Sci. Technol. An. Int. J., 38.","DOI":"10.1016\/j.jestch.2022.101322"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ghadami, R. (2025). An Intrusion Detection System in the Internet of Things with Federated Learning and Blockchain Integration. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-22074-3"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1109\/ACCESS.2017.2780250","article-title":"HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Obaidat, M.A., Choong, J.L., and Thakur, K. (2022). A Secure Authentication and Access Control Scheme for CoAP-Based IoT. Proceedings of the 5th Conference on Cloud Internet of Things (CIoT), IEEE.","DOI":"10.1109\/CIoT53061.2022.9766463"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ma, Y.W., Chen, Y.H., and Lin, C.H. (2025). A Novel Risk-Based Access Control Engine in Zero Trust Architecture for IoT Networks. Int. J. Inf. Secur., 24.","DOI":"10.1007\/s10207-025-01030-2"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MCOM.2017.1600363CM","article-title":"Security and Privacy for Cloud-Based IoT: Challenges","volume":"55","author":"Zhou","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ahsan, M.S., and Pathan, A.S.K. (2025). A Comprehensive Survey on the Requirements and Challenges of Access Control Models in the Internet of Things. IoT, 6.","DOI":"10.3390\/iot6010009"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ali, M.H., Jaber, M.M., Abd, S.K., Rehman, A., Awan, M.J., Dama\u0161evi\u010dius, R., and Bahaj, S.A. (2022). Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT). Electronics, 11.","DOI":"10.3390\/electronics11030494"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Srinivasa, S., Pedersen, J.M., and Vasilomanolakis, E. (2021). Open for Hire: Attack Trends and Misconfiguration Pitfalls of IoT Devices. Proceedings of the 21st ACM Internet Measurement Conference (IMC \u201921), ACM.","DOI":"10.1145\/3487552.3487833"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/JIOT.2021.3079916","article-title":"Consumer, Commercial, and Industrial IoT (In)Security: Attack Taxonomy and Case Studies","volume":"9","author":"Xenofontos","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lazzaro, S., De Angelis, V., Mandalari, A.M., and Buccafurri, F. (2025). A Black-Box Assessment of Authentication and Reliability in Consumer IoT Devices. Pervasive Mob. Comput., 110.","DOI":"10.1016\/j.pmcj.2025.102045"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Obaidat, M.A., Rawashdeh, M., Alja\u2019afreh, M., Abouali, M., Thakur, K., and Karime, A. (2024). Exploring IoT and Blockchain: A Comprehensive Survey on Security, Integration Strategies, Applications and Future Research Directions. Big Data Cogn. Comput., 8.","DOI":"10.20944\/preprints202409.1193.v1"},{"key":"ref_30","unstructured":"Ramos, J.L.H. (2020). Security and Privacy in the Internet of Things: Challenges and Solutions, SAGE Publications."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, J., Malialis, K., and Polycarpou, M.M. (2023). Autoencoder-Based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation. Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE.","DOI":"10.1109\/IJCNN54540.2023.10191328"},{"key":"ref_32","first-page":"1","article-title":"Autoencoder-Based Intrusion Detection in Critical Infrastructures","volume":"2","author":"Altunay","year":"2024","journal-title":"Curr. Trends Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, J., Zhang, J., Qian, R., Yuan, J., and Ren, Y. (2023). An Anomaly Detection Method for Wireless Sensor Networks Based on the Improved Isolation Forest. Appl. Sci., 13.","DOI":"10.3390\/app13020702"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Nguyen Cao Hoai, P., Ho, N., and Nguyen, T. (2025). MLMCID: Machine Learning for Multi-Class Intrusion Detection Using the CIC-IoT-2023 Dataset. International Conference on Data Analytics & Management, Springer.","DOI":"10.1007\/978-3-032-03527-1_9"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"82512","DOI":"10.1109\/ACCESS.2019.2923640","article-title":"An Adaptive Ensemble Machine Learning Model for Intrusion Detection","volume":"7","author":"Gao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7449","DOI":"10.1109\/TCE.2024.3458810","article-title":"Machine Learning-Based Network Intrusion Detection Optimization for Cloud Computing Environments","volume":"70","author":"Samriya","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"533","DOI":"10.32604\/iasc.2022.022259","article-title":"A Novel Hybrid Deep Learning Framework for Intrusion Detection Systems in WSN-IoT Networks","volume":"33","author":"Maheswari","year":"2022","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"19706","DOI":"10.1109\/JIOT.2022.3167005","article-title":"Intrusion Detection in the IoT Under Data and Concept Drifts: Online Deep Learning Approach","volume":"9","author":"Wahab","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.jss.2016.07.005","article-title":"A Survey on Feature Drift Adaptation: Definition, Benchmark, Challenges and Future Directions","volume":"127","author":"Barddal","year":"2017","journal-title":"J. Syst. Softw."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/IOTM.0001.2100012","article-title":"A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams","volume":"4","author":"Yang","year":"2021","journal-title":"IEEE Internet Things Mag."},{"key":"ref_41","first-page":"2346","article-title":"Learning Under Concept Drift: A Review","volume":"31","author":"Lu","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.eswa.2017.04.008","article-title":"On the Reliable Detection of Concept Drift from Streaming Unlabeled Data","volume":"82","author":"Sethi","year":"2017","journal-title":"Expert Syst. With Appl."},{"key":"ref_43","first-page":"16522","article-title":"Online Boosting Adaptive Learning under Concept Drift for Multistream Classification","volume":"38","author":"Yu","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_44","first-page":"281","article-title":"Machine Learning in Concept Drift Detection Using Statistical Measures","volume":"46","author":"Basulaim","year":"2024","journal-title":"Int. J. Comput. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bas, S., Kaya, K., Ak, E., and Oguducu, S.G. (2025). Adaptive Intrusion Detection for Evolving RPL IoT Attacks Using Incremental Learning. arXiv.","DOI":"10.1109\/CCNC65079.2026.11366367"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Desale, K.S., and Shinde, S.V. (2023). Concept Drift Detection and Adaption Framework Using Optimized Deep Learning and Adaptive Sliding Window Approach. Expert Syst., 40.","DOI":"10.1111\/exsy.13394"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.is.2019.02.003","article-title":"Boosting Decision Stumps for Dynamic Feature Selection on Data Streams","volume":"83","author":"Barddal","year":"2019","journal-title":"Inf. Syst."},{"key":"ref_48","unstructured":"Mahadevappa, P., Muzammal, S.M., and Murugesan, R.K. (2021). A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"147","DOI":"10.2478\/jaiscr-2025-0008","article-title":"A Novel Method for Drift Detection in Streaming Data Based on Measurement of Changes in Feature Ranks","volume":"15","author":"Porwik","year":"2025","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Shrivastava, K., Tiwari, M., and Chakrabati, P. (2025). A Federated Learning and Deep Learning-Based Intrusion Detection Mechanism for IoT Networks. Proceedings of the 2nd International Conference on Multidisciplinary Research and Innovations in Engineering (MRIE), IEEE.","DOI":"10.1109\/MRIE66930.2025.11156313"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"30164","DOI":"10.1109\/ACCESS.2024.3368377","article-title":"Explainable AI for Intrusion Detection Systems: LIME and SHAP Applicability on Multi-Layer Perceptron","volume":"12","author":"Gaspar","year":"2024","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Khan, N., Ahmad, K., Al Tamimi, A., Alani, M.M., Bermak, A., and Khalil, I. (2025). Explainable AI-Based Intrusion Detection Systems for Industry 5.0: A Survey. Information, 16.","DOI":"10.3390\/info16121036"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ahmad, Z., Khan, A.S., Shiang, C.W., Abdullah, J., and Ahmad, F. (2021). Network Intrusion Detection System: A Systematic Study of Machine Learning and Deep Learning Approaches. Trans. Emerg. Telecommun. Technol., 32.","DOI":"10.1002\/ett.4150"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ferrag, M.A., Maglaras, L., Moschoyiannis, S., and Janicke, H. (2020). Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study. J. Inf. Secur. Appl., 50.","DOI":"10.1016\/j.jisa.2019.102419"},{"key":"ref_55","unstructured":"and Dua, M. (2019). Machine Learning Approach to IDS: A Comprehensive Review. Proceedings of the 3rd International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"\u017dliobait\u0117, I., Pechenizkiy, M., and Gama, J. (2016). An Overview of Concept Drift Applications. Big Data Analysis: New Algorithms for a New Society, Springer.","DOI":"10.1007\/978-3-319-26989-4_4"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MCI.2015.2471196","article-title":"Learning in Nonstationary Environments: A Survey","volume":"10","author":"Ditzler","year":"2015","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ahsan, M.M., Mahmud, M.A.P., Saha, P.K., Gupta, K.D., and Siddique, Z. (2021). Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance. Technologies, 9.","DOI":"10.3390\/technologies9030052"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Farsi, A.A., Khan, A., and Bait-Suwailam, M.M. (2024). A Practical Evaluation of Intrusion Detection in IoT Networks Using Random Forest and Network Intrusion Detection Dataset. Proceedings of the 2nd International Conference on Computing and Data Analytics (ICCDA), IEEE.","DOI":"10.1109\/ICCDA64887.2024.10867402"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"167059","DOI":"10.1109\/ACCESS.2020.3022855","article-title":"Intrusion Detection Based on Autoencoder and Isolation Forest in Fog Computing","volume":"8","author":"Sadaf","year":"2020","journal-title":"IEEE Access"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"24120","DOI":"10.1109\/ACCESS.2020.2969428","article-title":"An Enhanced Anomaly Detection in Web Traffic Using a Stack of Classifier Ensemble","volume":"8","author":"Tama","year":"2020","journal-title":"IEEE Access"},{"key":"ref_62","unstructured":"Canadian Institute for Cybersecurity (2026, June 10). CICIoT2023 Dataset. Available online: https:\/\/ieee-dataport.org\/documents\/ciciot2023-dataset."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Neto, E.C.P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R., and Ghorbani, A.A. (2023). CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment. Sensors, 23.","DOI":"10.20944\/preprints202305.0443.v1"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"82721","DOI":"10.1109\/ACCESS.2019.2924045","article-title":"A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures","volume":"7","author":"Hassija","year":"2019","journal-title":"IEEE Access"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Moustafa, N., and Slay, J. (2015). UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15 Network Data Set). Proceedings of the Military Communications and Information Systems Conference (MilCIS), IEEE.","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/COMST.2019.2953364","article-title":"Security of the Internet of Things: Vulnerabilities, Attacks, and Countermeasures","volume":"22","author":"Butun","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_67","unstructured":"Cisco (2026, June 10). Cisco Annual Internet Report (2018\u20132023) White Paper. Cisco Systems. Available online: https:\/\/www.cisco.com\/c\/en\/us\/solutions\/collateral\/executive-perspectives\/annual-internet-report\/white-paper-c11-741490.html."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1093\/comjnl\/bxae023","article-title":"Concept Drift\u2013Based Intrusion Detection for Evolving Data Stream Classification in IDS: Approaches and Comparative Study","volume":"67","author":"Khanna","year":"2024","journal-title":"Comput. J."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Obaidat, M.A., Obeidat, S., Holst, J., Al Hayajneh, A., and Brown, J. (2020). A Comprehensive and Systematic Survey on the Internet of Things: Security and Privacy Challenges, Security Frameworks, Enabling Technologies, Threats, Vulnerabilities and Countermeasures. Computers, 9.","DOI":"10.3390\/computers9020044"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.cose.2019.06.005","article-title":"A Survey of Network-Based Intrusion Detection Data Sets","volume":"86","author":"Ring","year":"2019","journal-title":"Comput. Secur."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, T., and Wang, C. (2023). Hybrid Intrusion Detection System Based on Combination of Random Forest and Autoencoder. Symmetry, 15.","DOI":"10.3390\/sym15030568"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Alhowaide, A., Alsmadi, I., and Tang, J. (2021). Ensemble Detection Model for IoT IDS. Internet Things, 16.","DOI":"10.1016\/j.iot.2021.100435"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"115047","DOI":"10.1109\/ACCESS.2023.3323573","article-title":"An Explainable Ensemble Deep Learning Approach for Intrusion Detection in Industrial Internet of Things","volume":"11","author":"Shtayat","year":"2023","journal-title":"IEEE Access"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","article-title":"Ensemble Learning for Data Stream Analysis: A Survey","volume":"37","author":"Krawczyk","year":"2017","journal-title":"Inf. Fusion"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5117\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T04:18:44Z","timestamp":1786681124000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,12]]},"references-count":74,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26165117"],"URL":"https:\/\/doi.org\/10.3390\/s26165117","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,12]]}}}