A novel unified lightweight temporal-spatial transformer approach for intrusion detection in drone networks

The growing integration of drones across commercial, industrial, and civilian domains has introduced significant cybersecurity challenges, particularly due to the susceptibility of drone networks to a wide range of cyberattacks. Existing intrusion detection mechanisms often lack the adaptability, efficiency, and generalizability needed for the dynamic and resource-constrained environments in which drones operate. This paper proposes TSLT-Net, a novel lightweight and unified Temporal-Spatial Transformer-based intrusion detection system tailored specifically for drone networks. By leveraging self-attention mechanisms, TSLT-Net effectively models both temporal patterns and spatial dependencies in network traffic, enabling accurate detection of diverse intrusion types. The framework includes a streamlined preprocessing pipeline and supports both multiclass attack classification and binary anomaly detection within a single architecture. Extensive experiments conducted on the ISOT Drone Anomaly Detection Dataset, consisting of over 2.3 million labeled records, demonstrate TSLT-Net’s superior performance with 99.99% accuracy in multiclass detection and 100% in binary anomaly detection, all while maintaining a minimal memory footprint of just 0.04 MB and 9,722 trainable parameters. These results establish TSLT-Net as an effective and scalable solution for real-time drone cybersecurity, particularly suitable for deployment on edge devices in mission-critical UAV systems.