{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T06:27:46Z","timestamp":1779172066940,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>In recent years, with the development of the Internet of Things and distributed computing, the \u201cserver-edge device\u201d architecture has been widely deployed. This study focuses on leveraging autoencoder technology to address the binary classification problem in network intrusion detection, aiming to develop a lightweight model suitable for edge devices. Traditional intrusion detection models face two main challenges when directly ported to edge devices: inadequate computational resources to support large-scale models and the need to improve the accuracy of simpler models. To tackle these issues, this research utilizes the Extreme Learning Machine for its efficient training speed and compact model size to implement autoencoders. Two improvements over the latest related work are proposed: First, to improve data purity and ultimately enhance detection performance, the data are partitioned into multiple regions based on the prediction results of these autoencoders. Second, autoencoder characteristics are leveraged to further investigate the data within each region. We used the public dataset NSL-KDD to test the behavior of the proposed mechanism. The experimental results show that when dealing with multi-class attacks, the model\u2019s performance was significantly improved, and the accuracy and F1-Score were improved by 3.5% and 2.9%, respectively, maintaining its lightweight nature.<\/jats:p>","DOI":"10.3390\/computers13100269","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T07:47:05Z","timestamp":1728892025000},"page":"269","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Efficient Detection Mechanism of Network Intrusions in IoT Environments Using Autoencoder and Data Partitioning"],"prefix":"10.3390","volume":"13","author":[{"given":"Yiran","family":"Xiao","sequence":"first","affiliation":[{"name":"Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 8190395, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6074-2514","authenticated-orcid":false,"given":"Yaokai","family":"Feng","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka 8190000, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kouichi","family":"Sakurai","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka 8190000, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"ref_1","first-page":"71","article-title":"The internet of things (iot): An overview","volume":"5","author":"Pramod","year":"2015","journal-title":"Int. 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