Flocking control and intrusion detection in UAV networks
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Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as transformative technologies across numerous sectors, including commercial delivery, agricultural monitoring, disaster response, and military applications. Their ability to operate autonomously in diverse environments has led to rapid adoption and increasing deployment in swarm formations, where multiple UAVs work collectively to accomplish complex tasks. Despite their tremendous potential, this proliferation introduces two critical challenges: ensuring robust network security against cyber threats and developing efficient coordination mechanisms for drone swarms in dynamic environments. Traditional network security approaches for UAV systems face significant limitations. Existing intrusion detection datasets suffer from small sample sizes and unbalanced distributions, while conventional machine learning methods demand extensive labeled data that is both expensive and time-consuming to produce. Similarly, in the domain of swarm coordination, established rule-based methodologies like Craig Reynolds' Boid algorithm provide functional but limited frameworks that fail to capture the adaptive learning capabilities observed in natural flocking behaviors. This dissertation addresses these dual challenges through complementary research streams. First, to enhance UAV network security, we develop a novel approach combining Generative Adversarial Networks (GANs) with a Human-in-the-Loop (HITL) machine learning framework. The GAN component generates synthetic yet realistic network traffic samples to augment limited datasets, while the HITL methodology integrates human expertise to guide the learning process. Our comprehensive evaluation demonstrates that this integrated approach achieves intrusion detection accuracy of up to 99% while reducing the requirement for labeled data by up to 98%, presenting a cost-effective security solution for UAV networks. Building upon this security foundation, we then investigate advanced swarm coordination methods. We introduce a Multi-Agent Reinforcement Learning (MARL) extension to Boid modeling that enables individual drones to make autonomous decisions based on local environmental factors and collective swarm states. Unlike traditional rule-based models, our MARL-driven drones continuously optimize their behavior, mirroring the adaptability observed in natural flocks. Experimental results confirm that this approach outperforms conventional models in cohesion, alignment, and separation metrics, while exhibiting enhanced resilience against environmental variations and external perturbations. To further improve communication efficiency within these secure swarms, we develop the Dynamic BOID Routing Protocol (DBRP), which draws inspiration from biological flocking behaviors to enhance network connectivity and path stability. DBRP incorporates a motion control module that adjusts flight paths based on real-time swarm positioning and velocity, enabling adaptation to topological changes. Performance evaluations demonstrate that DBRP surpasses traditional UAV routing protocols in critical metrics such as latency, throughput, and packet loss rates, particularly in highly dynamic environments. Through these interconnected contributions, this dissertation advances both the theoretical understanding and practical implementation of secure, adaptive, and efficient UAV systems. The synergistic relationship between our security framework and swarm coordination methodologies offers a comprehensive approach to UAV network management with applications spanning civilian and defense sectors, potentially transforming how autonomous drone systems operate in complex real-world scenarios.
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Introduction -- Related work -- Methodology -- Experimental design -- Performance evaluation -- Conclusion and future work
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Ph.D. (Doctor of Philosophy)