{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:43:07Z","timestamp":1781016187287,"version":"3.54.1"},"reference-count":32,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:00:00Z","timestamp":1640649600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007446","name":"King Khalid University","doi-asserted-by":"publisher","award":["R. G. P. 1\/77\/42"],"award-info":[{"award-number":["R. G. P. 1\/77\/42"]}],"id":[{"id":"10.13039\/501100007446","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,12,28]]},"abstract":"<jats:p>Identification of anomaly and malicious traffic in the Internet of things (IoT) network is essential for IoT security. Tracking and blocking unwanted traffic flows in the IoT network is required to design a framework for the identification of attacks more accurately, quickly, and with less complexity. Many machine learning (ML) algorithms proved their efficiency to detect intrusion in IoT networks. But this ML algorithm suffers many misclassification problems due to inappropriate and irrelevant feature size. In this paper, an in-depth study is presented to address such issues. We have presented lightweight low-cost feature selection IoT intrusion detection techniques with low complexity and high accuracy due to their low computational time. A novel feature selection technique was proposed with the integration of rank-based chi-square, Pearson correlation, and score correlation to extract relevant features out of all available features from the dataset. Then, feature entropy estimation was applied to validate the relationship among all extracted features to identify malicious traffic in IoT networks. Finally, an extreme gradient ensemble boosting approach was used to classify the features in relevant attack types. The simulation is performed on three datasets, i.e., NSL-KDD, USNW-NB15, and CCIDS2017, and results are presented on different test sets. It was observed that on the NSL-KDD dataset, accuracy was approx. 97.48%. Similarly, the accuracy of USNW-NB15 and CCIDS2017 was approx. 99.96% and 99.93%, respectively. Along with that, state-of-the-art comparison is also presented with existing techniques.<\/jats:p>","DOI":"10.1155\/2021\/8091363","type":"journal-article","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T00:05:35Z","timestamp":1640736335000},"page":"1-13","source":"Crossref","is-referenced-by-count":17,"title":["Feature Entropy Estimation (FEE) for Malicious IoT Traffic and Detection Using Machine Learning"],"prefix":"10.1155","volume":"2021","author":[{"given":"Tarun Dhar","family":"Diwan","sequence":"first","affiliation":[{"name":"Chhattisgarh Swami Vivekananda Technical University, Bhilai, Chhattisgarh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siddartha","family":"Choubey","sequence":"additional","affiliation":[{"name":"Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. 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B","family":"Goyal","sequence":"additional","affiliation":[{"name":"City University, Petaling Jaya, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5852-1955","authenticated-orcid":true,"given":"Sajjad Shaukat","family":"Jamal","sequence":"additional","affiliation":[{"name":"Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3715-3882","authenticated-orcid":true,"given":"Piyush Kumar","family":"Shukla","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering Department, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, (Technological University of Madhya Pradesh), Bhopal 462023, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3113-4760","authenticated-orcid":true,"given":"Basant","family":"Tiwari","sequence":"additional","affiliation":[{"name":"Hawassa University, Awasa, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2924045"},{"key":"2","article-title":"Blockchain-based transformation: a gartner trend insight report","author":"R. 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