AI 中文总结
本文针对基于毫米波的对比学习人体活动识别系统，识别出三种标签翻转投毒攻击并提出防御措施，经原型系统验证有效，可扩展至其他无线HAR系统以强化安全设计。
AI 中文摘要
无线人体活动识别（HAR）凭借其非侵入性，有望变革医疗保健、虚拟现实和监控等多个领域。毫米波（mmWave）技术的出现显著提升了无线HAR系统的能力。本文首次针对监督对比学习场景下，基于毫米波的HAR系统对标签翻转投毒攻击的脆弱性开展系统性研究。我们识别出针对基于毫米波的对比HAR系统的三种标签投毒攻击，并提出相应的防御措施。在原型系统上对攻击的有效性及防御措施的效果进行了实验验证。这些攻击和防御措施可轻松扩展至其他无线HAR系统，从而在系统设计与部署中强化安全考量。
英文摘要
Wireless Human Activity Recognition (HAR), leveraging their non-intrusive nature, has the potential to revolutionize various sectors, including healthcare, virtual reality, and surveillance. The advent of millimeter wave (mmWave) technology has significantly enhanced the capabilities of wireless HAR systems. This paper presents the first systematic study on the vulnerabilities of mmWave-based HAR to label flipping poisoning attacks in the context of supervised contrastive learning. We identify three label poisoning attacks on the contrastive mmWave-based HAR and propose corresponding countermeasures. The efficacy of the attacks and also our countermeasures are experimentally validated on a prototype system. The attacks and countermeasures can be easily extended to other wireless HAR systems, thereby promoting security considerations in system design and deployment.
Comments11 pages, 18 figures. Published in Proceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '24)
Journal refProceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '24), Seoul, Republic of Korea, 2024, pp. 31-41
