{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:43:36Z","timestamp":1783611816538,"version":"3.55.0"},"reference-count":52,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"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>The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system\u2019s overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.<\/jats:p>","DOI":"10.3390\/s23198153","type":"journal-article","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T07:42:08Z","timestamp":1695973328000},"page":"8153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Novel Feature-Selection Algorithm in IoT Networks for Intrusion Detection"],"prefix":"10.3390","volume":"23","author":[{"given":"Anjum","family":"Nazir","sequence":"first","affiliation":[{"name":"Department of Computer Science, National University of Computer and Emerging Sciences (NUCES\u2014FAST), Karachi 75123, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6448-097X","authenticated-orcid":false,"given":"Zulfiqar","family":"Memon","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Computer and Emerging Sciences (NUCES\u2014FAST), Karachi 75123, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6603-3639","authenticated-orcid":false,"given":"Touseef","family":"Sadiq","sequence":"additional","affiliation":[{"name":"Centre for Artificial Intelligence Research, Department of Information and Communication Technology, University of Agder, Jon Lilletuns vei 9, 4879 Grimstad, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hameedur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Games Development, Faculty of Computing & AI, Air University, E9, Islamabad 44400, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3637-6977","authenticated-orcid":false,"given":"Inam Ullah","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, School of Engineering & Applied Sciences (SEAS), Isra University, Islamabad Campus, Islamabad 44400, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102481","DOI":"10.1016\/j.jnca.2019.102481","article-title":"A comprehensive survey on attacks, security issues and blockchain solutions for IoT and IIoT","volume":"149","author":"Sengupta","year":"2020","journal-title":"J. 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