CTGAN Enhanced Dataset for UAV Network Intrusion Detection

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. To resolve class imbalance and expand the dataset, we began with comprehensive data cleaning, removing incomplete, inconsistent, or irrelevant entries, including null values, NaNs, and infinite values. This preprocessing step ensured the integrity and quality of the dataset. Subsequently, we merged attack categories with similar features to streamline the classification process and enhance the dataset's consistency. Leveraging Conditional Tabular Generative Adversarial Networks (CTGAN), we augmented the dataset by generating synthetic samples that closely replicate the distribution of the original data. CTGAN effectively captures the underlying patterns and relationships within the data, producing high-quality synthetic instances that enhance both the quantity and diversity of the dataset. This augmentation significantly mitigates the issue of class imbalance, providing a more balanced representation of various intrusion types and enabling the training of more robust and generalizable IDS models.