EIDS-DTL: Edge-Based Intrusion Detection System for IoUAVs Using Metaheuristic Task Optimization and Deep Transfer Learning

The integration of Unmanned Aerial Vehicles (UAVs) with the Internet of Things (IoT), also known as IoUAVs, facilitates real-time data transmission and coordinated operations in critical applications such as smart agriculture, disaster response, and infrastructure monitoring. The growing development of IoT has, however, made IoUAVs vulnerable to emerging cyberattacks that could disrupt these essential services. Deep learning can detect hidden attack patterns, but power and processing constraints make it challenging for resource-constrained IoUAVs. Edge computing offloads real-time analysis tasks, but optimizing workloads with unpredictable connectivity and high latency requirements for intrusion detection remains challenging. To address these challenges, this paper proposes a novel Edge-Based Intrusion Detection System (EIDS) that introduces two key innovations. We developed a metaheuristic task optimization technique for the IoUAV edge environment to efficiently manage computational loads and resources. Second, a Deep Transfer Learning (DTL) technique optimized for intrusion detection minimizes training time and computational overhead. Our novel EIDS-DTL technology synergistically incorporates these components for powerful intrusion detection. Our method optimizes feature extraction from IoUAV network traffic by purifying, filtering, and normalizing data. By fine-tuning pre-trained models, the system achieves high accuracy in identifying malicious activity while ensuring optimal performance in resource-constrained environments. Experimental results on two benchmark datasets demonstrate classification accuracies of 98.95% and 99.27%, outperforming existing approaches by up to 5% in accuracy while maintaining high precision, recall, and F1 scores. The proposed method enhances accuracy and efficiency, providing an effective solution for IoUAV security and edge optimization.