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<p>In the realm of Internet of Drones (IoD) networks, IoT devices transmit environmental data via UAVs, enabling critical applications in disaster recovery, military operations, and environmental monitoring. However, safeguarding data collection by UAVs from malicious nodes presents a formidable challenge. This paper introduces a defense mechanism for IoD networks, centered around source location obfuscation and the identification of malicious nodes. The strategy employs UAVs as intermediaries, cloaking the origin of data by relaying it through virtual source UAVs to network users. Concurrently, the method employs adaptive IQR-based bandwidth consumption monitoring to expose malicious nodes that inject unauthorized data into UAV channels. A guard UAV frame- work is strategically integrated into the network architecture, augmenting malicious node detection by effectively luring and capturing suspicious traffic, enhanced with lightweight TinyML-based anomaly detection for real-time inference on resource-constrained platforms. Simulation-based evaluation across varying network sizes (20–100 UAVs, 200–1000 IoT devices) and malicious node ratios (5%–20%) demonstrates detection rates of 0.945–0.994 with false positive rates below 0.01, while maintaining an analytically estimated 6.6–8.5&#xa0;ms TinyML inference latency on ARM Cortex-M7-class guard UAVs (with end-to-end detection latency of 14.2&#xa0;ms). Statistical significance is confirmed through 20 independent runs with 95% confidence intervals.</p>

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UAV Guardians: Safeguarding Data Transmission from Malicious Nodes in a Drone Based IoT Network

  • 期刊论文

  • 发表日期：

    2026年7月22日

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  • Erfan Aghakarim Alamdar,
  • Sommayyeh Jafarali Jassbi

摘要

In the realm of Internet of Drones (IoD) networks, IoT devices transmit environmental data via UAVs, enabling critical applications in disaster recovery, military operations, and environmental monitoring. However, safeguarding data collection by UAVs from malicious nodes presents a formidable challenge. This paper introduces a defense mechanism for IoD networks, centered around source location obfuscation and the identification of malicious nodes. The strategy employs UAVs as intermediaries, cloaking the origin of data by relaying it through virtual source UAVs to network users. Concurrently, the method employs adaptive IQR-based bandwidth consumption monitoring to expose malicious nodes that inject unauthorized data into UAV channels. A guard UAV frame- work is strategically integrated into the network architecture, augmenting malicious node detection by effectively luring and capturing suspicious traffic, enhanced with lightweight TinyML-based anomaly detection for real-time inference on resource-constrained platforms. Simulation-based evaluation across varying network sizes (20–100 UAVs, 200–1000 IoT devices) and malicious node ratios (5%–20%) demonstrates detection rates of 0.945–0.994 with false positive rates below 0.01, while maintaining an analytically estimated 6.6–8.5 ms TinyML inference latency on ARM Cortex-M7-class guard UAVs (with end-to-end detection latency of 14.2 ms). Statistical significance is confirmed through 20 independent runs with 95% confidence intervals.