Raed Alharthi ^*

• University of Hafr Al Batin, Hafar Al Batin, Saudi Arabia

The emergence of small-drone technology has revolutionized the way we use drones. Small drones leverage the Internet of Things (IoT) to deliver location-based navigation services, making them versatile tools for various applications. Both UAV (Unmanned Aerial Vehicle) communication networks and smart grid communication protocols share several similarities, particularly in terms of their architecture, the nature of the data they handle, and the security challenges they face. To ensure the safe, secure, and reliable operation of both, it is imperative to establish a secure and dependable network infrastructure and to develop and implement robust security and privacy mechanisms tailored to the specific needs of this domain. The research evaluates the performance of deep learning 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. By demonstrating its superior performance, this work highlights the importance of tailored security mechanisms in safeguarding UAV technology against evolving cyber threats. Ultimately, this research contributes to the growing body of knowledge on UAV security, emphasizing the necessity of high-quality datasets and advanced models in ensuring the safe, secure, and reliable operation of UAV systems across various industries.