Leveraging Metaheuristics with Deep Learning for DDoS Attack Detection in SDN based IoT Networks

Leveraging Metaheuristics with Deep Learning for DDoS Attack Detection in SDN based IoT Networks | 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 Leveraging Metaheuristics with Deep Learning for DDoS Attack Detection in SDN based IoT Networks Reem Alkanhel, Ahsan Rafiq, Mohammed Saleh, Ali Muthanna, Daljeet Singh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4934617/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 Internet of Things (IoT) and Software-Defined Networking (SDN) are essential technology that enhances network administrations’ performance. Conveniently, SDN offers a simple and centralized system for administering a huge count of IoT devices and significantly decreases the capacity of network administrators. SDN mostly concentrates on upper-level control and network management, but IoT purposes are carrying devices collected to enable share and monitoring real-time performances with network connectivity. However, there is still a requirement for improving security performance in SDN depending on IoT networks to mitigate attacks containing IoT devices like Distributed Denial of Service (DDoS). With this stimulus, this study designs metaheuristics with deep learning-based DDoS attack detection model (MDL-DDoSAM) in the SDN-IoT environment. The presented MDL-DDoSAM technique mainly aims to identify the occurrence of DDoS attacks in the SDN-IoT environment. To accomplish this, the presented MDL-DDoSAM technique initially preprocesses the networking data. In addition, the presented MDL-DDoSAM technique designs the jellyfish search optimizer-based feature selection (JFSO-FS) technique to elect feature subsets. For DDoS attack detection and classification, the Bidirectional CUDA Deep Neural Network Long Short-Term Memory (BiCD-LSTM) model was used. Its hyperparameters are optimally adjusted by an improved whale optimization algorithm (IWOA). The significant DDoS attack detection performance of the MDL-DDoSAM system was tested utilizing a benchmark database, and the results indicate improved performance over other existing models. Software defined networks DDoS attacks Internet of Things Deep learning Parameter tuning 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-4934617","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341871731,"identity":"98677837-afcb-45b9-a12d-98c17a26475b","order_by":0,"name":"Reem 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Conveniently, SDN offers a simple and centralized system for administering a huge count of IoT devices and significantly decreases the capacity of network administrators. SDN mostly concentrates on upper-level control and network management, but IoT purposes are carrying devices collected to enable share and monitoring real-time performances with network connectivity. However, there is still a requirement for improving security performance in SDN depending on IoT networks to mitigate attacks containing IoT devices like Distributed Denial of Service (DDoS). With this stimulus, this study designs metaheuristics with deep learning-based DDoS attack detection model (MDL-DDoSAM) in the SDN-IoT environment. The presented MDL-DDoSAM technique mainly aims to identify the occurrence of DDoS attacks in the SDN-IoT environment. To accomplish this, the presented MDL-DDoSAM technique initially preprocesses the networking data. 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