Undermining User Privacy on Mobile Devices Using AI
Undermining User Privacy on Mobile Devices Using AI
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DOI：
10.1145/3321705.3329804
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Berk Gülmezoglu;A. Zankl;M. Caner Tol;Saad Islam;T. Eisenbarth;B. Sunar
中科院分区：
文献类型：
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作者：
Berk Gülmezoglu;A. Zankl;M. Caner Tol;Saad Islam;T. Eisenbarth;B. Sunar
在过去的几年里，文献表明，利用现代处理器的微体系结构的攻击对用户隐私构成了严重威胁。这是因为应用程序在处理器中留下了不同的足迹，恶意软件可以利用这些足迹来推断用户的活动。在这项工作中，我们证明了先进的人工智能技术可以极大地增强这些推理攻击。特别是，我们重点分析了ARM处理器的末级缓存(LLC)中的活动。我们使用一种简单的基于Prime+探测器的监控技术来获取缓存跟踪，并使用包括卷积神经网络在内的深度学习方法对其进行分类。我们在一部现成的Android手机上演示了我们的方法，在不到一分钟的时间里，从一个没有特权的零权限应用程序发动了一次成功的攻击。该应用程序检测正在运行的应用程序、打开的网站和流媒体视频，准确率高达98%，分析阶段最多6秒。这是可能的，因为深度学习补偿了由固有的噪声LLC监控和不利的高速缓存特性引起的测量干扰。总之，我们的结果表明，由于先进的人工智能技术，推理攻击在实践中变得非常容易执行。这再次要求采取对策，限制微架构泄漏，保护手机应用程序，特别是那些重视用户隐私的应用程序。
Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to user privacy. This is because applications leave distinct footprints in the processor, which malware can use to infer user activities. In this work, we show that these inference attacks can greatly be enhanced with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with deep learning methods including convolutional neural networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zero-permission app in well under a minute. The app detects running applications, opened websites, and streaming videos with up to 98% accuracy and a profiling phase of at most 6 seconds. This is possible, as deep learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users.