{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T22:31:04Z","timestamp":1788993064063,"version":"build-2803163510"},"reference-count":48,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at Najran University","award":["NU\/DRP\/SERC\/12\/56"],"award-info":[{"award-number":["NU\/DRP\/SERC\/12\/56"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Internet of Things (IoT) has brought significant advancements that have connected our world more closely than ever before. However, the growing number of connected devices has also increased the vulnerability of IoT networks to several types of attacks. In this paper, we present an approach for detecting attacks on IoT networks using a combination of two convolutional neural networks (CNN-CNN). The first CNN model is leveraged to select the significant features that contribute to IoT attack detection from the raw data on network traffic. The second CNN utilizes the features identified by the first CNN to build a robust detection model that accurately detects IoT attacks. The proposed approach is evaluated using the BoT IoT 2020 dataset. The results reveal that the proposed approach achieves 98.04% detection accuracy, 98.09% precision, 99.85% recall, 98.96% recall, and a 1.93% false positive rate (FPR). Furthermore, the proposed approach is compared with other deep learning algorithms and feature selection methods; the results show that it outperforms these algorithms.<\/jats:p>","DOI":"10.3390\/s23146507","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T01:02:23Z","timestamp":1689728543000},"page":"6507","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":102,"title":["CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2987-8856","authenticated-orcid":false,"given":"Basim","family":"Alabsi","sequence":"first","affiliation":[{"name":"Applied College, Najran University, King Abdulaziz Street, Najran P.O. Box 1988, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7026-6408","authenticated-orcid":false,"given":"Mohammed","family":"Anbar","sequence":"additional","affiliation":[{"name":"National Advanced IPv6 (NAv6) Centre, Universiti Sains Malaysia, Gelugor 11800, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaza","family":"Rihan","sequence":"additional","affiliation":[{"name":"Applied College, Najran University, King Abdulaziz Street, Najran P.O. Box 1988, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sarker, I.H., Khan, A.I., Abushark, Y.B., and Alsolami, F. (2022). Internet of things (iot) security intelligence: A comprehensive overview, machine learning solutions and research directions. Mob. Netw. 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