Robust Defences against Adversarial Machine Learning for UAV Systems

针对无人机系统对抗性机器学习的稳健防御

• 批准号: LP230100083
• 负责人: Prof Willy Susilo
• 金额: $ 30.39万
• 依托单位: University of Wollongong
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP230100083
• 关键词: Robust; Defences; against; Adversarial; Machine

This project aims to investigate robust defences for Unmanned Aerial Vehicle (UAV) systems to protect them against adversarial Machine Learning (ML) attacks. This project expects to generate new knowledge in the area of cybersecurity using innovative approaches to safeguard UAV systems from attacks that exploit vulnerabilities in ML models. The expected outcomes of this project include improve techniques for understanding and developing robust ML models and enhanced capacity to design secure UAV systems. This should provide significant benefits, such as improving the security of UAV technology and increasing the reliable use of UAVs for transport and logistics services to support urban and regional communities in Australia.

该项目旨在调查无人驾驶汽车（UAV）系统的强大防御能力，以保护它们免受对抗机器学习（ML）的攻击。该项目希望使用创新的方法在网络安全领域产生新的知识，以保护无人机系统免受攻击的攻击，从而利用ML模型中的脆弱性。该项目的预期结果包括改进理解和开发强大的ML模型的技术，并增强了设计安全无人机系统的能力。这将提供巨大的好处，例如提高无人机技术的安全性，并增加无人机用于运输和物流服务以支持澳大利亚城市和地区社区的可靠使用。