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<p>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&#xa0;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&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月21日

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  • Tarun Kumar Biswas,
  • Ashrafun Zannat,
  • Waqas Ishtiaq,
  • Md. Alamgir Hossain

摘要

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.