A Novel Deep Learning Approach for Intrusion Detection in Maritime Radar Networks

A Novel Deep Learning Approach for Intrusion Detection in Maritime Radar 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 A Novel Deep Learning Approach for Intrusion Detection in Maritime Radar Networks Md. Samiul Islam, Md. Alamgir Hossain This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7611107/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract In recent years, maritime radar networks have become essential for ensuring the safety and security of maritime operations. However, with the increased interconnectivity of these systems, they have also become vulnerable to cyber-attacks, posing significant risks to critical infrastructure. Traditional intrusion detection systems (IDS) often struggle to detect sophisticated and evolving attacks in real-time due to their reliance on manual feature extraction and shallow machine learning techniques. This research addresses this gap by introducing MARINERNet, a deep learning-based intrusion detection system designed specifically for maritime radar networks. The proposed system uses a novel architecture that integrates 1D convolutional layers, squeeze-and-excitation blocks, and residual connections to automatically extract relevant features from raw radar network data, enhancing detection accuracy without manual intervention. MARINERNet is evaluated on both binary and multiclass classification tasks, demonstrating state-of-the-art performance with an accuracy of 98.52%, and 100% for anomaly detection. The approach is scalable, capable of handling large datasets, and adaptable to real-time intrusion detection, making it suitable for deployment in dynamic radar environments. This research not only provides an effective solution for detecting intrusions in maritime radar networks but also contributes to the broader field of cybersecurity by offering a robust, deep learning-based approach that can be applied to other network systems. Physical sciences/Engineering Physical sciences/Mathematics and computing Maritime Radar Networks Maritime Radar Intrusion Detection Deep Learning Anomaly Detection Radar Network Traffic Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 Oct, 2025 Reviews received at journal 19 Oct, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers invited by journal 25 Sep, 2025 Editor invited by journal 17 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 14 Sep, 2025 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-7611107","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":525073330,"identity":"c90044e0-34ee-4f9c-bf74-90558e9b0582","order_by":0,"name":"Md. 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