Towards Blockchain-Based Cybersecurity Framework for Internet of Things-Enabled Smart Infrastructure Using Feature Engineering with Quantum Deep Learning Model

Towards Blockchain-Based Cybersecurity Framework for Internet of Things-Enabled Smart Infrastructure Using Feature Engineering with Quantum Deep Learning Model | 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 Towards Blockchain-Based Cybersecurity Framework for Internet of Things-Enabled Smart Infrastructure Using Feature Engineering with Quantum Deep Learning Model M. Revathi, R. Lavanya, SV. Shri Bharathi, G Kirankumar, Cheolhee Yoon, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7078371/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 The fast propagation of Internet of Things (IoT) networks in smart cities have presented many advantages such as improving urban efficacy, automation, and sustainability. However, these unified methods also pose important cybersecurity tasks, such as unauthorized access, data breaches, and cyberattacks that can deal with crucial infrastructure. As smart cities trust deeply real-time data automation and exchange, certifying the integrity, security, and confidentiality of these methods is vital. Conventional security devices, like firewalls and traditional encryption models frequently drop short owing to the spread nature and resource restraints of IoT devices. To tackle these tasks, industry specialists and researchers have developed advanced cybersecurity tactics, with blockchain (BC) technology and artificial intelligence (AI)-based models. Recently, the AI-driven security solutions permit real-world anomaly detection (AD) by analyzing cyber-attack designs and mechanizing risk mitigation events. This study presents the Secure Cybersecurity Framework for Smart Infrastructure Using Feature Engineering and Quantum Deep Learning Model (SCFSI-FEQDLM) methodology. This paper aims to propose a robust cybersecurity model for protecting IoT-based smart infrastructure using BC technology. Initially, the BC technology-based IoT is applied to enhance security, transparency, and efficiency in smart infrastructure systems. Furthermore, the data standardization applies min-max normalization to convert input data into a suitable format. Moreover, the feature selection (FS) process is performed by correlation analysis model, mutual information method, and principal component analysis technique to select the most significant features from a dataset. For the classification process, the SCFSI-FEQDLM method implements quantum long short-term memory with stochastic gradient descent optimizer for improving the classification performance. The incorporatin of feature engineering process with hyperparameter tuned quantum classification model helps in the accomplishment of improved detection rate. The experimental evaluation of the SCFSI-FEQDLM model was examined on a benchmark dataset. The extensive results highlight the significant solution of the SCFSI-FEQDLM approach when compared to recent models. Physical sciences/Engineering Physical sciences/Mathematics and computing Artificial Intelligence Blockchain Cybersecurity Internet of Things Smart Infrastructure Feature Engineering Quantum Computing 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-7078371","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":497405979,"identity":"32555597-13c5-4a06-923d-ba9459ca041d","order_by":0,"name":"M. Revathi","email":"","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"","lastName":"Revathi","suffix":""},{"id":497405980,"identity":"d2db271e-b4f8-4487-9d2f-7983ed8f372e","order_by":1,"name":"R. 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