Towards Reliable IoT Security: A Deterministic Arithmetic Optimization Algorithm for Wrapper-Based Feature Selection in Intrusion Detection Systems

Towards Reliable IoT Security: A Deterministic Arithmetic Optimization Algorithm for Wrapper-Based Feature Selection in Intrusion Detection Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Towards Reliable IoT Security: A Deterministic Arithmetic Optimization Algorithm for Wrapper-Based Feature Selection in Intrusion Detection Systems Taha M.O. Alakhras, Waheed A. H. M Ghanem, Farizah Yunus, Sanaa A. A Ghaleb, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8305469/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Today use computer networks all the time for everything—our phones, computers, internet of thing (IoT), and cloud services. Because of this, networks often get attacked by things like denial of service (DoS), user to remote attack (U2R) We try to stop these attacks with Intrusion Detection Systems (IDSs). However, today's IDSs struggle to find brand-new types of attacks. To make them work better, we first need to pick out only the most useful features of information before the system runs. This paper introduces a Deterministic version of the Arithmetic Optimization Algorithm (DAOA) to solve the feature selection problem in classification. The classifier employs K-Nearest Neighbors (KNN) using a wrapper-based approach. to find the optimal solutions. In contrast, all previous studies have introduced a probabilistic version of the Arithmetic Optimization Algorithm (BAOA). This study uses NF-UNSW-NB15-V2 dataset as benchmark datasets from the collection by the university of Queensland The results demonstrate that DAOA outperformed the Binary Arithmetic Optimization Algorithm(BAOA), Binary Grey Wolf Optimizer (GWO), Binary Particle Swarm Optimization (BPSO), and Binary Harmony Search optimization (HS),Binary Ant Colony Optimization for Real-valued domains (ACOR), when various performance metrics were used, including classification accuracy, selected features, The tested algorithms were ranked using the Friedman Test, and pairwise comparisons were performed using the Wilcoxon Signed-Rank Test. After running the algorithms for 30 iterations and 20 epochs, the results showed that the DAOA achieved the highest classification accuracy while selecting the smallest feature set compared to all other tested algorithms. arithmetic optimization algorithm feature selection binary optimization classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8305469","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566597490,"identity":"f22e2f79-d043-4016-97a9-cf96ce35560c","order_by":0,"name":"Taha M.O. 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