{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T15:35:49Z","timestamp":1776008149466,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China Fundamental Research Funds for the Central 321 Universities","award":["N2017003 and N182808003"],"award-info":[{"award-number":["N2017003 and N182808003"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>The Internet of Things (IoT) has emerged as a new technological world connecting billions of devices. Despite providing several benefits, the heterogeneous nature and the extensive connectivity of the devices make it a target of different cyberattacks that result in data breach and financial loss. There is a severe need to secure the IoT environment from such attacks. In this paper, an SDN-enabled deep-learning-driven framework is proposed for threats detection in an IoT environment. The state-of-the-art Cuda-deep neural network, gated recurrent unit (Cu- DNNGRU), and Cuda-bidirectional long short-term memory (Cu-BLSTM) classifiers are adopted for effective threat detection. We have performed 10 folds cross-validation to show the unbiasedness of results. The up-to-date publicly available CICIDS2018 data set is introduced to train our hybrid model. The achieved accuracy of the proposed scheme is 99.87%, with a recall of 99.96%. Furthermore, we compare the proposed hybrid model with Cuda-Gated Recurrent Unit, Long short term memory (Cu-GRULSTM) and Cuda-Deep Neural Network, Long short term memory (Cu- DNNLSTM), as well as with existing benchmark classifiers. Our proposed mechanism achieves impressive results in terms of accuracy, F1-score, precision, speed efficiency, and other evaluation metrics.<\/jats:p>","DOI":"10.3390\/s21144884","type":"journal-article","created":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T21:18:52Z","timestamp":1626643132000},"page":"4884","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["A Hybrid Deep Learning-Driven SDN Enabled Mechanism for Secure Communication in Internet of Things (IoT)"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7831-8188","authenticated-orcid":false,"given":"Danish","family":"Javeed","sequence":"first","affiliation":[{"name":"Software College, Northeastern University, Shenyang 110169, China"}]},{"given":"Tianhan","family":"Gao","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang 110169, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1326-7292","authenticated-orcid":false,"given":"Muhammad","family":"Khan","sequence":"additional","affiliation":[{"name":"Riphah Institute of Science and Engineering, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3974-2207","authenticated-orcid":false,"given":"Ijaz","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Institute of Computer Sciences and Information Technology (ICS\/IT), The University of Agriculture, Peshawar 25130, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mrabet, H., Belguith, S., Alhomoud, A., and Jemai, A. 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