Ryu-IDS: Intrusion Detection System for Modern Networks

Ryu-IDS: Intrusion Detection System for Modern 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 Article Ryu-IDS: Intrusion Detection System for Modern Networks M. Sami Ataa, Eman E. Sanad, Reda A. El-khoribi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6092259/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 As SDN continues to play a crucial role in modern network infrastructures, particularly in IoT-driven smart city applications, securing these environments against cyber threats is essential. This paper presents the deployment and evaluation of an Intrusion Detection System (IDS) within a Software-Defined Networking (SDN) environment, emphasizing its real-world impact on network performance. The IDS is implemented as an SDN-native application within the Ryu controller. A simulated SDN testbed is constructed using Mininet and Open vSwitch (OvS) to assess the IDS’s effect on key performance metrics such as latency, throughput, packet loss, CPU usage, and memory consumption. The results reveal that IDS increased network latency by an average of 0.016ms. Throughput decreased by approximately 100 kBps. Additionally, CPU usage rose by 5%, while memory usage increased by less than 1%. Unlike many existing studies that focus solely on Deep Learning (DL) model metrics, this research establishes a benchmark for evaluating the impact of DL-based IDS deployment in SDN environments. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Software Software-defined networking (SDN) Internet of Things (IoT) Deep Learning (DL) Intrusion Detection System (IDS) Ryu Controller 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-6092259","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":448176252,"identity":"75f0deec-79dd-4cea-98ad-19285d0c9d46","order_by":0,"name":"M. 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