Lightweight Neural Anomaly Detection for Resource-Constrained Edge-ICN Environments: A Systematic Literature Review

Lightweight Neural Anomaly Detection for Resource-Constrained Edge-ICN Environments: A Systematic Literature Review | 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 Systematic Review Lightweight Neural Anomaly Detection for Resource-Constrained Edge-ICN Environments: A Systematic Literature Review Isaac Kofi Nti, Miriyala Sai Manikanta, Clark Alex, Lee Jo Ning This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8002082/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 Edge computing and Information-Centric Networks (ICN) are converging to support low-latency, scalable, and content-aware network infrastructures. This shift introduces new challenges for real-time anomaly detection on resource-constrained edge devices. While prior studies have addressed ICN security and lightweight detection models in Internet of Things (IoT) settings, few have systematically explored lightweight neural models for anomaly detection at the edge. This paper conducts a Systematic Literature Review (SLR) that examines general edge-based approaches and extracts transferable insights for ICN anomaly contexts. We answer four key research questions: (1) the types of lightweight neural models used for anomaly detection on edge devices, (2) how these models are adapted for edge resource constraints, (3) the strategies used for integrating detection within ICN architectures, and (4) the main research challenges and future directions in this space. We analyzed 71 peer-reviewed studies (2017–2025) to identify trends in model architectural, optimization techniques, deployment strategies, and performance evaluation. Our synthesis highlights the predominant use of CNN-based and hybrid models optimized via pruning, quantization, and knowledge distillation. Special focus is given to content-centric anomalies and caching-related threats unique to ICN. We identify several research gaps, including scarcity of ICN-specific datasets, underutilization of explainable AI techniques, and limited focus on real-time and energy-efficient deployments. This review provides a structured foundation that may inform future research on secure, intelligent, and efficient anomaly detection systems in next-generation ICN-edge infrastructures. Artificial Intelligence and Machine Learning Lightweight neural networks anomaly detection edge computing resource-constrained AI model optimization sparse models interpretability low-latency inference information-centric networks Full Text Additional Declarations The authors declare no competing interests. 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-8002082","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":538280346,"identity":"c37159eb-1b95-404e-a95b-48e01c91da48","order_by":0,"name":"Isaac Kofi 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