Surf-Snooping: USB crosstalk leakage attacks on wireless charging

IF 4.8 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yue Hou , Xinyan Zhou , Huakang Xia , Jian Wang , Haiming Chen
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Abstract

Various mobile devices such as smartphones, tablets, and computers have been increasingly incorporated with wireless charging technology. Nevertheless, this widespread adoption of wireless chargers has raised substantial concerns regarding privacy and security. The Universal Serial Bus (USB), serving as the primary power supply port, is widely acknowledged as a significant source of privacy vulnerabilities. This article introduces Surf-Snooping, a side-channel attack that is aimed at exploiting vulnerabilities in wireless charging systems. Through monitoring the voltage fluctuations of a nearby USB charging port, an attacker can eavesdrop on the detailed activities and operations that are happening during smartphone charging (e.g., PIN code and text input), even without engaging in data communication. With a trained model, Surf-Snooping exhibits a 100% accuracy on device model identification, while the activity recognition accuracy can reach up to 86.7%, 89.9%, and 81.7% for password recognition, application identification, and keystroke inference, respectively. It is noteworthy that Surf-Snooping achieves accuracies of 92.3%, 98%, and 94.5% for the three types of activity categorization when the phone is fully charged. We also validate the privacy leakage risk of Surf-Snooping with different scenarios, and our work reveals an inherent flaw in the current implementation of wireless charging systems. It provides enhanced obfuscation and stability, requires no physical interference with the charging infrastructure, and remains effective throughout the entire charging cycle.
冲浪窥探:USB串扰泄漏攻击无线充电
各种移动设备,如智能手机、平板电脑和电脑已经越来越多地与无线充电技术相结合。然而,无线充电器的广泛采用引起了人们对隐私和安全的极大关注。作为主要供电端口的通用串行总线(USB)被广泛认为是隐私漏洞的重要来源。本文介绍了Surf-Snooping,这是一种旨在利用无线充电系统漏洞的侧信道攻击。通过监控附近USB充电端口的电压波动,攻击者可以在不进行数据通信的情况下,窃听到智能手机充电过程中的详细活动和操作(例如PIN码和文本输入)。在经过训练的模型下,Surf-Snooping在设备模型识别上的准确率达到100%,在密码识别、应用识别和按键推理上的活动识别准确率分别达到86.7%、89.9%和81.7%。值得注意的是,当手机充满电时,Surf-Snooping对三种类型的活动分类的准确率分别为92.3%、98%和94.5%。我们还在不同的场景下验证了Surf-Snooping的隐私泄露风险,我们的工作揭示了当前无线充电系统实现的固有缺陷。它提供了增强的混淆和稳定性,不需要对充电基础设施进行物理干扰,并在整个充电周期中保持有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Security
Computers & Security 工程技术-计算机:信息系统
CiteScore
12.40
自引率
7.10%
发文量
365
审稿时长
10.7 months
期刊介绍: Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world. Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.
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