Federated Learning Framework for Intrusion Detection System in Internet of Vehicles with Memory-Augmented Deep Autoencoder

Federated Learning Framework for Intrusion Detection System in Internet of Vehicles with Memory-Augmented Deep Autoencoder | 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 Federated Learning Framework for Intrusion Detection System in Internet of Vehicles with Memory-Augmented Deep Autoencoder G. Hima Bindu, Deepthi Reddy Dasari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5007599/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 Intrusion detection systems (IDS) are crucial for maintaining the security and integrity of Internet of Vehicles (IoV) configurations. However, traditional IDS systems face issues such as scalability, flexibility in changing IoV settings, and privacy concerns due to centralized data collection. The increasing number of networked cars in the IoV poses significant security concerns, including identifying and mitigating cyberattacks. We need a more effective, privacy-preserving IDS solution, and Federated Learning (FL) emerges as a promising option. The paper suggests using a Federated Learning Framework memory-augmented deep autoencoder for intrusion detection systems (FLF-MADAE) on the IoV to make it safer and fix common IDS issues at the same time. However, autoencoders can generalize and reconstruct anomalies, potentially causing them to go undetected. To address this issue, we propose a memory module named MADAE, which retrieves encoded versions from the encoder and employs a query to select the optimal memory objects for reconstruction. The training phase involves updating memory contents and encouraging them to reflect the usual data items. We tested the effectiveness of the proposed strategy on the car hacking and CSE-CIC-IDS-2018 intrusion detection datasets. Experimental results show that on the CSE-CIC-IDS-2018 dataset, FLF-MADAE has the highest accuracy level of 99.12% and an F1 score of 99.21%; for the car hacking dataset, MADAE has the highest accuracy level of 99.24% and an F1 score of 98.77%. Federated Learning Deep Learning memory-augmented deep autoencoder Classification Internet of Vehicles 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-5007599","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":360936878,"identity":"98f5537b-779b-4997-b253-ed8fb9c8d3b7","order_by":0,"name":"G. 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However, traditional IDS systems face issues such as scalability, flexibility in changing IoV settings, and privacy concerns due to centralized data collection. The increasing number of networked cars in the IoV poses significant security concerns, including identifying and mitigating cyberattacks. We need a more effective, privacy-preserving IDS solution, and Federated Learning (FL) emerges as a promising option. The paper suggests using a Federated Learning Framework memory-augmented deep autoencoder for intrusion detection systems (FLF-MADAE) on the IoV to make it safer and fix common IDS issues at the same time. However, autoencoders can generalize and reconstruct anomalies, potentially causing them to go undetected. To address this issue, we propose a memory module named MADAE, which retrieves encoded versions from the encoder and employs a query to select the optimal memory objects for reconstruction. 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