{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T15:35:50Z","timestamp":1776008150319,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T00:00:00Z","timestamp":1645056000000},"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 Grant Number N182808003."],"award-info":[{"award-number":["N2017003 and Grant Number N182808003."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the new advancements in Internet of Things (IoT) and its applications in different sectors, such as the industrial sector, by connecting billions of devices and instruments, IoT has evolved as a new paradigm known as the Industrial Internet of Things (IIoT). Nonetheless, its benefits and applications have been approved in different areas, but there are possibilities for various cyberattacks because of its extensive connectivity and diverse nature. Such attacks result in financial loss and data breaches, which urge a consequential need to secure IIoT infrastructure. To combat the threats in the IIoT environment, we proposed a deep-learning SDN-enabled intelligent framework. A hybrid classifier is used for threat detection purposes, i.e., Cu-LSTMGRU + Cu-BLSTM. The proposed model achieved a better detection accuracy with low false-positive rate. We have conducted 10-fold cross-validation to show the unbiasdness of the results. The proposed scheme results are compared with Cu-DNNLSTM and Cu-DNNGRU classifiers, which were tested and trained on the same dataset. We have further compared the proposed model with other existing standard classifiers for a thorough performance evaluation. Results achieved by our proposed scheme are impressive with respect to speed efficiency, F1 score, accuracy, precision, and other evaluation metrics.<\/jats:p>","DOI":"10.3390\/s22041582","type":"journal-article","created":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T20:26:41Z","timestamp":1645129601000},"page":"1582","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["A Hybrid Intelligent Framework to Combat Sophisticated Threats in Secure Industries"],"prefix":"10.3390","volume":"22","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 Taimoor","family":"Khan","sequence":"additional","affiliation":[{"name":"Riphah Institute of Science and Engineering, Islamabad 44000, Pakistan"}]},{"given":"Duaa","family":"Shoukat","sequence":"additional","affiliation":[{"name":"Riphah Institute of Science and Engineering, Islamabad 44000, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2489","DOI":"10.1109\/COMST.2020.3011208","article-title":"A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities","volume":"22","author":"Tange","year":"2020","journal-title":"IEEE Commun. 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