RoboLSTM-IDS: multi-dataset evaluation of deep learning framework for UAV network

The growing deployment of uncrewed aerial vehicles (UAV) in autonomous and networked missions has heightened their exposure to both cyber and cyber-physical attacks, underscoring the need for intelligent and lightweight intrusion detection systems (IDS) solutions. This study introduces RoboLSTM-IDS, a deep anomaly-based framework that combines robust feature engineering with temporal sequence modeling for UAV network security. Leveraging Robust Optimization-Based Tabular Feature Engineering (ROBOTa), a robust optimization-based feature selection technique—the system extracts stable, high-impact features from complex UAV telemetry and communication data. These are modeled using a Long Short-Term Memory network to capture sequential attack dynamics. Comprehensive experiments conducted on five benchmark datasets, including real-world UAV cyber-physical data (T-ITS), CICIDS-2017, UNSW-NB15, and their CTGAN-augmented variants, demonstrate that RoboLSTM-IDS consistently outperforms traditional machine learning and deep learning baselines. It achieves up to 99.62% accuracy and 0.997 AUC, while maintaining low false positive rates and real-time execution performance. Unlike conventional IDS models that are computationally heavy, proposed model achieves a 6× smaller model size, 3× lower memory footprint, and significantly reduced inference latency. These results confirm RoboLSTM-IDS as an effective and scalable IDS solution tailored for next-generation UAV ecosystems.