<p>The growing adoption of unmanned aerial vehicles (UAV) in civilian applications has highlighted the necessity of securing these systems against potential cyber threats. This paper presents a distilled pruned network for unmanned aerial vehicles (UAV-DiPNID), a specialized intrusion detection system designed to monitor and detect unauthorized or malicious activities involving UAVs. The proposed method leverages a combination of knowledge distillation and model pruning to develop an effective and efficient intrusion detection system based on a deep neural network. Knowledge distillation transfers knowledge from a larger, more complex model (teacher) to a smaller, simpler model (student) to enhance inference time and model compactness. Additionally, model pruning is employed to reduce the model’s size by removing unnecessary connections and weights, further optimizing resource usage. The experimentation and evaluation of the proposed method demonstrate its effectiveness in terms of intrusion detection accuracy, inference time, and model size. Extensive experiments on the UAV-IDS 2020 dataset demonstrate that UAV-DiPNID achieves an average intrusion detection accuracy of 99.61%, outperforming the state-of-the-art benchmark of 99.37%. Furthermore, our method reduces the inference time by up to 80.70% and the model size by up to 90% compared to conventional convolutional neural network-based approaches. These results highlight the superiority of UAV-DiPNID compared to baseline intrusion detection systems for UAVs, making it well-suited for real-time applications in resource-constrained environments. Furthermore, the accuracy of intrusion detection remains high, showcasing the model’s robustness in capturing complex patterns and features.</p>