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The emergence of small-drone technology has revolutionized the way we use drones. Small drones leverage the Internet of Things (IoT) to provide precise navigation and location-based services, making them versatile tools for various applications. However, small drones’ structural and design vulnerabilities expose them to significant security and privacy threats. To ensure the secure and reliable operation of small drones, developing a robust network infrastructure and implementing tailored security and privacy mechanisms is essential. The research evaluates the performance of deep learning (DL) models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and ConvLSTM, in detecting intrusions within UAV communication networks. The study utilizes five diverse and realistic datasets, namely KDD Cup-99, NSL-KDD, WSN-DS, CICIDS2017, and Drone datasets, to simulate real-world intrusion scenarios. Notably, the ConvLSTM model consistently achieves an accuracy of 99.99%, showcasing its potential in securing UAVs from cyber threats. This research underscores the significance of robust cybersecurity measures in the ever-expanding realm of UAV technology and highlights the pivotal role played by high-quality datasets in enhancing UAV security. As UAVs become increasingly integral to various industries, this study contributes to ensuring their safety, security, and reliability in the face of evolving cyber risks.