{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:08:35Z","timestamp":1775146115929,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The Internet of Things (IoT) ecosystem is rapidly expanding. It is driven by continuous innovation but accompanied by increasingly sophisticated cybersecurity threats. Protecting IoT devices from these emerging vulnerabilities has become a critical priority. This study addresses the limitations of existing IoT threat detection methods, which often struggle with the dynamic nature of IoT environments and the growing complexity of cyberattacks. To overcome these challenges, a novel hybrid architecture combining Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Deep Neural Networks (DNN) is proposed for accurate and efficient IoT threat detection. The model\u2019s performance is evaluated using the IoT-23 and Edge-IIoTset datasets, which encompass over ten distinct attack types. The proposed framework achieves a remarkable 99% accuracy on both datasets, outperforming existing state-of-the-art IoT cybersecurity solutions. Advanced optimization techniques, including model pruning and quantization, are applied to enhance deployment efficiency in resource-constrained IoT environments. The results highlight the model\u2019s robustness and its adaptability to diverse IoT scenarios, which address key limitations of prior approaches. This research provides a robust and efficient solution for IoT threat detection, establishing a foundation for advancing IoT security and addressing the evolving landscape of cyber threats while driving future innovations in the field.<\/jats:p>","DOI":"10.3390\/computers14020058","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T04:37:04Z","timestamp":1739335024000},"page":"58","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Hybrid CNN\u2013BiLSTM\u2013DNN Approach for Detecting Cybersecurity Threats in IoT Networks"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1495-9972","authenticated-orcid":false,"given":"Bright Agbor","family":"Agbor","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, University of Uyo, Uyo 520101, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2535-4492","authenticated-orcid":false,"given":"Bliss Utibe-Abasi","family":"Stephen","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Uyo, Uyo 520101, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4888-5461","authenticated-orcid":false,"given":"Philip","family":"Asuquo","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Uyo, Uyo 520101, Nigeria"}]},{"given":"Uduak Onofiok","family":"Luke","sequence":"additional","affiliation":[{"name":"School of Sustainability, Civil and Environmental Engineering, University of Surrey, Guildford GU2 7XH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0523-4828","authenticated-orcid":false,"given":"Victor","family":"Anaga","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Uyo, Uyo 520101, Nigeria"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"94668","DOI":"10.1109\/ACCESS.2021.3089681","article-title":"Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey","volume":"9","author":"Zaman","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"188082","DOI":"10.1109\/ACCESS.2020.3029847","article-title":"An Empirical Study on System Level Aspects of Internet of Things (IoT)","volume":"8","author":"Swamy","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"499","DOI":"10.3844\/ajeassp.2020.499.515","article-title":"A Comprehensive Review on Internet of Things (IoT) and its Implications in the Mining Industry","volume":"13","author":"Molaei","year":"2020","journal-title":"Am. J. Eng. Appl. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Quy, V.K., Hau, N.V., Anh, D.V., Quy, N.M., Ban, N.T., Lanza, S., Randazzo, G., and Muzirafuti, A. (2022). IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Appl. Sci., 12.","DOI":"10.3390\/app12073396"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1007\/s42853-020-00078-3","article-title":"A Review of the Applications of the Internet of Things (IoT) for Agricultural Automation","volume":"45","author":"Kim","year":"2020","journal-title":"J. Biosyst. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kassim, M.R.M. (2020, January 17\u201319). IoT Applications in Smart Agriculture: Issues and Challenges. Proceedings of the 2020 IEEE Conference on Open Systems (ICOS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICOS50156.2020.9293672"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Zamar, M.D., Abad-Segura, E., V\u00e1zquez-Cano, E., and L\u00f3pez-Meneses, E. (2020). IoT Technology Applications-Based Smart Cities: Research Analysis. Electronics, 9.","DOI":"10.3390\/electronics9081246"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kanellopoulos, D., Sharma, V.K., Panagiotakopoulos, T., and Kameas, A. (2023). Networking Architectures and Protocols for IoT Applications in Smart Cities: Recent Developments and Perspectives. Electronics, 12.","DOI":"10.3390\/electronics12112490"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.dsx.2020.04.041","article-title":"Internet of Things (IoT) applications to fight against COVID-19 pandemic","volume":"14","author":"Singh","year":"2020","journal-title":"Diabetes Metab. Syndr. Clin. Res. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"122877","DOI":"10.1016\/j.jclepro.2020.122877","article-title":"Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future","volume":"274","author":"Patrono","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_11","unstructured":"Kaspersky (2024, December 06). Kaspersky Unveils an Overview of IoT-Related Threats in 2023. Kaspersky Press Releases. Available online: https:\/\/www.kaspersky.com\/about\/press-releases\/kaspersky-unveils-an-overview-of-iot-related-threats-in-2023."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1007\/s11277-020-07446-4","article-title":"Internet of Things (IoT), Applications and Challenges: A Comprehensive Review","volume":"114","author":"Khanna","year":"2020","journal-title":"Wirel. Pers. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"59353","DOI":"10.1109\/ACCESS.2021.3073408","article-title":"Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review","volume":"9","author":"Mishra","year":"2021","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"120331","DOI":"10.1109\/ACCESS.2020.3006358","article-title":"Security Analysis of IoT Devices by Using Mobile Computing: A Systematic Literature Review","volume":"8","author":"Liao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4455","DOI":"10.1109\/JIOT.2019.2950469","article-title":"Cost-effective video summarization using deep CNN with hierarchical weighted fusion for IoT surveillance networks","volume":"7","author":"Muhammad","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7021","DOI":"10.1007\/s11227-020-03560-z","article-title":"Bidirectional LSTM-RNN-Based Hybrid Deep Learning Frameworks for Univariate Time Series Classification","volume":"77","author":"Khan","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.isprsjprs.2019.01.015","article-title":"Recurrently Exploring Class-Wise Attention in a Hybrid Convolutional and Bidirectional LSTM Network for Multi-Label Aerial Image Classification","volume":"149","author":"Hua","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Algethami, S.A., and Alshamrani, S.S. (2024). A Deep Learning-Based Framework for Strengthening Cybersecurity in Internet of Health Things (IoHT) Environments. Appl. Sci., 14.","DOI":"10.3390\/app14114729"},{"key":"ref_19","first-page":"3765","article-title":"One Dimensional Conv-BiLSTM Network with Attention Mechanism for IoT Intrusion Detection","volume":"77","author":"Omarov","year":"2023","journal-title":"Comput. Math. Methods Eng."},{"key":"ref_20","first-page":"200298","article-title":"A Comparative Evaluation of Intrusion Detection Systems on the Edge-IIoT-2022 Dataset","volume":"20","author":"Alyilieli","year":"2023","journal-title":"Intell. Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103167","DOI":"10.1016\/j.cose.2023.103167","article-title":"Network Anomaly Detection Methods in IoT Environments via Deep Learning: A Fair Comparison of Performance and Robustness","volume":"128","author":"Bovenzi","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.procs.2024.08.027","article-title":"Machine Learning for DoS Attack Detection in IoT Systems","volume":"241","author":"Kikissagbe","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ahli, A., Raza, A., Ovaz, K., and Akpinar, M. (2023, January 20\u201323). Binary and Multi-Class Classification on the IoT-23 Dataset. Proceedings of the 2023 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates.","DOI":"10.1109\/ASET56582.2023.10180848"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104034","DOI":"10.1016\/j.cose.2024.104034","article-title":"IoT-PRIDS: Leveraging Packet Representations for Intrusion Detection in IoT Networks","volume":"146","author":"Zohourian","year":"2024","journal-title":"Comput. Secur."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.comcom.2021.05.024","article-title":"Internet of Things Attack Detection Using Hybrid Deep Learning Model","volume":"176","author":"Sahu","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103980","DOI":"10.1016\/j.jnca.2024.103980","article-title":"A Lightweight SEL for Attack Detection in IoT\/IIoT Networks","volume":"230","author":"Abdulkareem","year":"2024","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Riaz, S., Latif, S., Usman, S.M., Ullah, S.S., Algarni, A.D., Yasin, A., Anwar, A., Elmannai, H., and Hussain, S. (2022). Malware Detection in Internet of Things (IoT) Devices Using Deep Learning. Sensors, 22.","DOI":"10.3390\/s22239305"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"99837","DOI":"10.1109\/ACCESS.2022.3206425","article-title":"CNN-LSTM: Hybrid Deep Neural Network for Network Intrusion Detection System","volume":"10","author":"Halbouni","year":"2022","journal-title":"IEEE Access"},{"key":"ref_29","first-page":"101322","article-title":"A Hybrid CNN+LSTM-Based Intrusion Detection System for Industrial IoT Networks","volume":"38","author":"Altunay","year":"2023","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102777","DOI":"10.1016\/j.asej.2024.102777","article-title":"A Deep Learning-Based Novel Hybrid CNN-LSTM Architecture for Efficient Detection of Threats in the IoT Ecosystem","volume":"15","author":"Nazir","year":"2024","journal-title":"Ain Shams Eng. J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bhandari, G., Lyth, A., Shalaginov, A., and Gr\u00f8nli, T.-M. (2023). Distributed Deep Neural-Network-Based Middleware for Cyber-Attacks Detection in Smart IoT Ecosystem: A Novel Framework and Performance Evaluation Approach. Electronics, 12.","DOI":"10.3390\/electronics12020298"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.dcan.2022.09.008","article-title":"An Ensemble Deep Learning Model for Cyber Threat Hunting in Industrial Internet of Things","volume":"9","author":"Yazdinejad","year":"2023","journal-title":"Digit. Commun. Netw."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"101252","DOI":"10.1016\/j.iot.2024.101252","article-title":"Enhancing Cybersecurity in Edge IIoT Networks: An Asynchronous Federated Learning Approach with a Deep Hybrid Detection Model","volume":"27","author":"Bukhari","year":"2024","journal-title":"Internet Things"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6430","DOI":"10.1109\/ACCESS.2021.3140015","article-title":"Generative Deep Learning to Detect Cyberattacks for the IoT-23 Dataset","volume":"10","author":"Abdalgawad","year":"2022","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zixu, T., Liyanage, K.S.K., and Gurusamy, M. (2020, January 7\u201311). Generative Adversarial Network and Auto Encoder-Based Anomaly Detection in Distributed IoT Networks. Proceedings of the GLOBECOM 2020\u20142020 IEEE Global Communications Conference, Taipei, Taiwan.","DOI":"10.1109\/GLOBECOM42002.2020.9348244"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","article-title":"Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset","volume":"100","author":"Koroniotis","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Alani, M.M., and Miri, A. (2022). Towards an Explainable Universal Feature Set for IoT Intrusion Detection. Sensors, 22.","DOI":"10.3390\/s22155690"},{"key":"ref_38","unstructured":"Moustafa, N. (2019). ToN_IoT Datasets. IEEE Dataport."},{"key":"ref_39","unstructured":"Garcia, S., Parmisano, A., and Erquiaga, M.J. (2020). IoT-23: A Labeled Dataset with Malicious and Benign IoT Network Traffic (Version 1.0.0) [Data set]. Zenodo."},{"key":"ref_40","unstructured":"Alfares, H., and Banimelhem, O. (2006, January 4\u20136). Comparative Analysis of Machine Learning Techniques for Handling Imbalance in IoT-23 Dataset for Intrusion Detection Systems. Proceedings of the 11th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), Dubai, United Arab Emirates."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2520","DOI":"10.1016\/j.procs.2023.01.226","article-title":"Efficient Feature Evaluation Approach for a Class-Imbalanced Dataset Using Machine Learning","volume":"218","author":"Thiyam","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"40281","DOI":"10.1109\/ACCESS.2022.3165809","article-title":"Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning","volume":"10","author":"Ferrag","year":"2022","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Hakak, S., and Ghorbani, A.A. (2019, January 1\u20133). Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy. Proceedings of the IEEE 53rd International Carnahan Conference on Security Technology, Chennai, India.","DOI":"10.1109\/CCST.2019.8888419"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"100673","DOI":"10.1016\/j.prime.2024.100673","article-title":"Two-Step Data Clustering for Improved Intrusion Detection System Using CICIoT2023 Dataset","volume":"9","author":"Gheni","year":"2024","journal-title":"e-Prime-Adv. Electr. Eng. Electron. Energy"},{"key":"ref_45","first-page":"2139","article-title":"Detection of Real-Time Distributed Denial-of-Service (DDoS) Attacks on Internet of Things (IoT) Networks Using Machine Learning Algorithms","volume":"80","author":"Mahdi","year":"2024","journal-title":"Comput. Mater. Contin."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"100936","DOI":"10.1016\/j.iot.2023.100936","article-title":"Enhancing IoT Network Security Through Deep Learning-Powered Intrusion Detection System","volume":"24","author":"Bakhsh","year":"2023","journal-title":"Internet Things"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liang, T., Glossner, J., Wang, L., Shi, S., and Zhang, X. (2021). Pruning and Quantization for Deep Neural Network Acceleration: A Survey. arXiv.","DOI":"10.1016\/j.neucom.2021.07.045"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"139113","DOI":"10.1109\/ACCESS.2024.3465631","article-title":"Advances in Pruning and Quantization for Natural Language Processing","volume":"12","author":"Bibi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_49","unstructured":"Zeeshan, M. (2024). Efficient Deep Learning Models for Edge IoT Devices\u2014A Review, Department of Electrical Engineering, Indian Institute of Technology Patna. Available online: https:\/\/d197for5662m48.cloudfront.net\/documents\/publicationstatus\/216349\/preprint_pdf\/fd256b0205683d43c9f61e8327fec378.pdf."},{"key":"ref_50","first-page":"1","article-title":"BinaryConnect: Training Deep Neural Networks with Binary Weights During Propagations","volume":"12","author":"Courbariaux","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_51","unstructured":"Han, S., Mao, H., and Dally, W.J. (2016). Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhuang, B., Shen, C., Tan, M., Liu, L., and Reid, I. (2017). Towards Effective Low-Bitwidth Convolutional Neural Networks. arXiv.","DOI":"10.1109\/CVPR.2018.00826"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.aci.2018.08.003","article-title":"Classification Assessment Methods","volume":"17","author":"Tharwat","year":"2021","journal-title":"Appl. Comput. Inform."},{"key":"ref_54","first-page":"6","article-title":"Classification Model Evaluation Metrics","volume":"12","author":"Vujovic","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Sukhdeve, D.S.R., and Sukhdeve, S.S. (2023). Google Cloud Platform for Data Science: A Crash Course, Springer.","DOI":"10.1007\/978-1-4842-9688-2"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/2\/58\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:30:29Z","timestamp":1760027429000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/2\/58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,10]]},"references-count":55,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["computers14020058"],"URL":"https:\/\/doi.org\/10.3390\/computers14020058","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,10]]}}}