Intrusion detection in Unmanned Aerial Vehicle (UAV) networks is crucial for maintaining the security and integrity of autonomous operations. However, the effectiveness of intrusion detection systems (IDS) is often compromised by the scarcity and imbalance of available datasets, which limits the ability to train accurate and reliable machine learning models. To address these challenges, we present the "CTGAN-Enhanced Dataset for UAV Network Intrusion Detection", a meticulously curated and augmented dataset designed to improve the performance of IDS in UAV environments.