Mitigation and Performance Analysis of Internal Routing Attacks in RPL-Based IoT Networks under Different Node Deployments

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Abstract The IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is the de facto standard for multi-hop communication in the Internet of Things (IoT). Despite extensive research on individual attack types, the combined influence of node-placement geometry and routing attacks on RPL resilience remains poorly quantified. This paper presents a comprehensive, topology-aware vulnerability model built through thirty independent Contiki 2.7 (Cooja 1.2) simulations for each of four internal attacks—flooding, selective forwarding, DIO suppression, and replay—across three representative geometries: random, linear, and elliptical. Each scenario employed twenty legitimate Sky motes and five malicious nodes for ten-minute runs, producing a statistically validated dataset of delivery , loss, and energy metrics. Results reveal a decisive geometric effect: while random layouts sustain ≈ 97 % Packet Delivery Ratio (PDR) with moderate Energy per Correctly delivered Packet (ECPP 0.1 J), elliptical networks collapse under replay with PDR 4.9 J. Two-way ANOVA and post-hoc tests confirm topology as a dominant factor (p < 0.001, η 2 = 0.78). Finally, a lightweight topology-aware hardening mechanism is proposed, integrating adaptive Trickle timers, duplicate-sequence monitors, and energy-biased parent selection. The findings establish topology geometry as a first-order design variable for secure, energy-efficient RPL deployment in next-generation IoT and industrial networks.
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Mitigation and Performance Analysis of Internal Routing Attacks in RPL-Based IoT Networks under Different Node Deployments | 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 Mitigation and Performance Analysis of Internal Routing Attacks in RPL-Based IoT Networks under Different Node Deployments Vaibhav Ajay, Virender Ranga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8480474/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract The IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is the de facto standard for multi-hop communication in the Internet of Things (IoT). Despite extensive research on individual attack types, the combined influence of node-placement geometry and routing attacks on RPL resilience remains poorly quantified. This paper presents a comprehensive, topology-aware vulnerability model built through thirty independent Contiki 2.7 (Cooja 1.2) simulations for each of four internal attacks—flooding, selective forwarding, DIO suppression, and replay—across three representative geometries: random, linear, and elliptical. Each scenario employed twenty legitimate Sky motes and five malicious nodes for ten-minute runs, producing a statistically validated dataset of delivery , loss, and energy metrics. Results reveal a decisive geometric effect: while random layouts sustain ≈ 97 % Packet Delivery Ratio (PDR) with moderate Energy per Correctly delivered Packet (ECPP 0.1 J), elliptical networks collapse under replay with PDR 4.9 J. Two-way ANOVA and post-hoc tests confirm topology as a dominant factor (p < 0.001, η 2 = 0.78). Finally, a lightweight topology-aware hardening mechanism is proposed, integrating adaptive Trickle timers, duplicate-sequence monitors, and energy-biased parent selection. The findings establish topology geometry as a first-order design variable for secure, energy-efficient RPL deployment in next-generation IoT and industrial networks. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers invited by journal 12 Jan, 2026 Editor assigned by journal 10 Jan, 2026 Submission checks completed at journal 01 Jan, 2026 First submitted to journal 30 Dec, 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. 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