{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:05:35Z","timestamp":1782317135571,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:00:00Z","timestamp":1652140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Taif University","award":["TURSP-2020\/292"],"award-info":[{"award-number":["TURSP-2020\/292"]}]},{"name":"Taif University","award":["PNURSP2022R193"],"award-info":[{"award-number":["PNURSP2022R193"]}]},{"name":"Princess Nourah bint Abdulrahman University","award":["TURSP-2020\/292"],"award-info":[{"award-number":["TURSP-2020\/292"]}]},{"name":"Princess Nourah bint Abdulrahman University","award":["PNURSP2022R193"],"award-info":[{"award-number":["PNURSP2022R193"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Internet of Things (IoT) is a widely used technology in automated network systems across the world. The impact of the IoT on different industries has occurred in recent years. Many IoT nodes collect, store, and process personal data, which is an ideal target for attackers. Several researchers have worked on this problem and have presented many intrusion detection systems (IDSs). The existing system has difficulties in improving performance and identifying subcategories of cyberattacks. This paper proposes a deep-convolutional-neural-network (DCNN)-based IDS. A DCNN consists of two convolutional layers and three fully connected dense layers. The proposed model aims to improve performance and reduce computational power. Experiments were conducted utilizing the IoTID20 dataset. The performance analysis of the proposed model was carried out with several metrics, such as accuracy, precision, recall, and F1-score. A number of optimization techniques were applied to the proposed model in which Adam, AdaMax, and Nadam performance was optimum. In addition, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. All experimental analysis indicates that the accuracy of the proposed approach is high and more robust than existing DL-based algorithms.<\/jats:p>","DOI":"10.3390\/s22103607","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T21:52:11Z","timestamp":1652219531000},"page":"3607","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9973-640X","authenticated-orcid":false,"given":"Safi","family":"Ullah","sequence":"first","affiliation":[{"name":"Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6289-8248","authenticated-orcid":false,"given":"Jawad","family":"Ahmad","sequence":"additional","affiliation":[{"name":"School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muazzam A.","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan"},{"name":"Pakistan Academy of Sciences, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0412-7127","authenticated-orcid":false,"given":"Eman H.","family":"Alkhammash","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9070-6821","authenticated-orcid":false,"given":"Myriam","family":"Hadjouni","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7121-495X","authenticated-orcid":false,"given":"Yazeed Yasin","family":"Ghadi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi 122612, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2822-1708","authenticated-orcid":false,"given":"Faisal","family":"Saeed","sequence":"additional","affiliation":[{"name":"DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3392-9970","authenticated-orcid":false,"given":"Nikolaos","family":"Pitropakis","sequence":"additional","affiliation":[{"name":"School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9483","DOI":"10.1109\/ACCESS.2022.3142848","article-title":"A Survey on the Role of IoT in Agriculture for the Implementation of Smart Livestock Environment","volume":"10","author":"Farooq","year":"2022","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103906","DOI":"10.1109\/ACCESS.2021.3094024","article-title":"Design and development of a deep learning-based model for anomaly detection in IoT networks","volume":"9","author":"Ullah","year":"2021","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mezni, H., Driss, M., Boulila, W., Atitallah, S.B., Sellami, M., and Alharbi, N. 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