2024利用生成式AI增强自主系统的安全性和韧性白皮书（英文版）_41页_3mb

Overview

This report examines the transformative potential of Generative AI (GenAI) in enhancing security, resilience, and safety for drones, autonomous systems, and human interactions within the Zero Trust ecosystem. The focus is on applications for individual drones, drone swarms, human-AI collaboration, and cybersecurity.

GenAI Techniques

The document outlines several key GenAI techniques used in these applications, including:

• Transformers and LLMs: For complex text understanding and natural language generation.
• GANs: For synthetic data generation, realistic content creation, and detecting rare events.
• VAEs and GFM: For anomaly detection, compression of data, and probabilistic modeling.
• NFMs: For transforming complex data distributions.
  Specific applications improved the performance of autonomous navigation, decision-making, fault detection, and predictive maintenance.

GenAI Security Applications

GenAI enhances Zero Trust in the following areas:

• Individual Drones: For self-awareness, anomaly detection, autonomy, predictive maintenance, fault management, and safe landing. Techniques like GANs, VAEs, and conditional GANs offer models for state estimation and fill missing data gaps.
• Fleet Operations: Through swarm intelligence to combine information from multiple drones, improving object tracking and reliability. Techniques like CGANs, transformers, and diffusion models are used for task allocation and communication efficiency.
• Cybersecurity: For threat monitoring, policy management, threat simulations, and improving intrusion detection systems beyond traditional methods.

Challenges

Scaling GenAI use is hindered by several factors:

• Cost: High computational expenses, including inference and training.
• Compute: Transformers, in particular, require substantial computational resources.
• Adaptation: Maintaining accuracy in adapting to changing threat landscapes or operational environments.
• Ethical/Regulatory: Greater opacity and potential for unintended consequences.
• Privacy: Difficulty in protecting sensitive data gathered from drones.

Conclusion

Integrative GenAI frameworks promise to revolutionize drone security but require significant investment and careful management. Key future research areas include continuous multi-device authentication, model explainability, secure communication, standard integration practices, and thorough solution validation.