文献类型：会议论文

英文题名：Domain-Adaptive Power Profiling Analysis Strategy for the Metaverse

作者：Li, Xiang[1]; Yang, Ning[1]; Liu, Weifeng[2]; Chen, Aidong[3,4]; Zhang, Yanlong[5]; Wang, Shuo[5]; Zhou, Jing[5]

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China; [2] Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China; [3] College of Robotics, Beijing Union University, Beijing, China; [4] Research Center for Multi-Intelligent Systems, Beijing Union University, Beijing, China; [5] Information System Laboratory, Beijing Microelectronics Technology Institution, Beijing, China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[3]College of Robotics, Beijing Union University, Beijing, China|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

语种：英文

外文关键词：Deep learning - Learning systems - Side channel attack - Virtual reality

摘要：In the surge of the digital era, the metaverse, as a groundbreaking concept, has become a focal point in the technology sector. It is reshaping human work and life patterns, carving out a new realm of virtual and real interaction. However, the rapid development of the metaverse brings along novel challenges in security and privacy. In this multifaceted and complex technological environment, data protection is of paramount importance. The innovative capabilities of high-end devices and functions in the metaverse, owing to advanced integrated circuit technology, face unique threats from side-channel analysis (SCA), potentially leading to breaches in user privacy. Addressing the issue of domain differences caused by different hardware devices, which impact the generalizability of the analysis model and the accuracy of analysis, this paper proposes a strategy of portability power profiling analysis (PPPA). Combining domain adaptation and deep learning techniques, it models and calibrates the domain differences between the profiling and target devices, enhancing the model's adaptability in different device environments. Experiments show that our method can recover the correct key with as few as 389 power traces, effectively recovering keys across different devices. This paper underscores the effectiveness of cross-device SCA, focusing on the adaptability and robustness of analysis models in different hardware environments, thereby enhancing the security of user data privacy in the metaverse environment. ? 2024 John Wiley & Sons Ltd.