{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T10:41:42Z","timestamp":1777286502059,"version":"3.51.4"},"reference-count":79,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T00:00:00Z","timestamp":1627603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57\u20132.6% in terms of the model\u2019s accuracy, while at the same time reducing the FAR by 0.23\u20137.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16\u201335 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model\u2019s performance but helped in decreasing the overall model\u2019s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99\u20133.45% in terms of the detection accuracy considering only the top five categorical and numerical features.<\/jats:p>","DOI":"10.3390\/app11157050","type":"journal-article","created":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T12:59:24Z","timestamp":1627649964000},"page":"7050","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":144,"title":["Anomaly Detection Using Deep Neural Network for IoT Architecture"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8530-864X","authenticated-orcid":false,"given":"Zeeshan","family":"Ahmad","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia"},{"name":"Department of Electrical Engineering, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3924-3646","authenticated-orcid":false,"given":"Adnan","family":"Shahid Khan","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8092-4665","authenticated-orcid":false,"given":"Kashif","family":"Nisar","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Malaysia"},{"name":"Department of Computer Science and Engineering, Hanyang University, Seoul 04763, Korea"}]},{"given":"Iram","family":"Haider","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1348-5804","authenticated-orcid":false,"given":"Rosilah","family":"Hassan","sequence":"additional","affiliation":[{"name":"Centre for Cyber Security, Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3531-220X","authenticated-orcid":false,"given":"Muhammad Reazul","family":"Haque","sequence":"additional","affiliation":[{"name":"Faculty of Computing & Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia"}]},{"given":"Seleviawati","family":"Tarmizi","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8657-3800","authenticated-orcid":false,"given":"Joel J. P. C.","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Post-Graduation Program on Electrical Engineering, Federal University of Piau\u00ed (UFPI), Teresina 64049-550, PI, Brazil"},{"name":"Covilh\u00e3 Delegation, Instituto de Telecomunica\u00e7\u00f5es, 6201-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1109\/JSEN.2020.2977352","article-title":"A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications","volume":"21","author":"Harb","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Haider, I., Khan, K.B., Haider, M.A., Saeed, A., and Nisar, K. (2020, January 5\u20137). Automated Robotic System for Assistance of Isolated Patients of Coronavirus (COVID-19). 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