{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:24:53Z","timestamp":1783571093902,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,25]],"date-time":"2022-12-25T00:00:00Z","timestamp":1671926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Telecommunication networks are growing exponentially due to their significant role in civilization and industry. As a result of this very significant role, diverse applications have been appeared, which require secured links for data transmission. However, Internet-of-Things (IoT) devices are a substantial field that utilizes the wireless communication infrastructure. However, the IoT, besides the diversity of communications, are more vulnerable to attacks due to the physical distribution in real world. Attackers may prevent the services from running or even forward all of the critical data across the network. That is, an Intrusion Detection System (IDS) has to be integrated into the communication networks. In the literature, there are numerous methodologies to implement the IDSs. In this paper, two distinct models are proposed. In the first model, a custom Convolutional Neural Network (CNN) was constructed and combined with Long Short Term Memory (LSTM) deep network layers. The second model was built about the all fully connected layers (dense layers) to construct an Artificial Neural Network (ANN). Thus, the second model, which is a custom of an ANN layers with various dimensions, is proposed. Results were outstanding a compared to the Logistic Regression algorithm (LR), where an accuracy of 97.01% was obtained in the second model and 96.08% in the first model, compared to the LR algorithm, which showed an accuracy of 92.8%.<\/jats:p>","DOI":"10.3390\/s23010206","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T03:03:31Z","timestamp":1672110211000},"page":"206","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["An Anomaly Intrusion Detection for High-Density Internet of Things Wireless Communication Network Based Deep Learning Algorithms"],"prefix":"10.3390","volume":"23","author":[{"given":"Emad Hmood","family":"Salman","sequence":"first","affiliation":[{"name":"Department of Communications Engineering, College of Engineering, University of Diyala, Baquba 32001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1516-8231","authenticated-orcid":false,"given":"Montadar Abas","family":"Taher","sequence":"additional","affiliation":[{"name":"Department of Communications Engineering, College of Engineering, University of Diyala, Baquba 32001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1360-9005","authenticated-orcid":false,"given":"Yousif I.","family":"Hammadi","sequence":"additional","affiliation":[{"name":"Department of Medical Instruments Engineering Techniques, Bilad Alrafidain University College, Diyala 32001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar Abdulkareem","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Department of Communications Engineering, College of Engineering, University of Diyala, Baquba 32001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0213-8145","authenticated-orcid":false,"given":"Ammar","family":"Muthanna","sequence":"additional","affiliation":[{"name":"Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 Saint Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrey","family":"Koucheryavy","sequence":"additional","affiliation":[{"name":"Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 Saint Petersburg, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,25]]},"reference":[{"key":"ref_1","first-page":"2021","article-title":"IoT Connections to Reach 83 Billion by 2024, Driven by Maturing Industrial Use Cases","volume":"10","author":"Smith","year":"2020","journal-title":"Accessed Apr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7154587","DOI":"10.1155\/2021\/7154587","article-title":"Intrusion Detection in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection","volume":"2021","author":"Awotunde","year":"2021","journal-title":"Wirel. 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