Fear and Logging in the Internet of Things
Fear and Logging in the Internet of Things
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发表时间：
2018
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通讯作者：
Qi Wang;Wajih Ul Hassan;Adam Bates;Carl A. Gunter
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作者：
Qi Wang;Wajih Ul Hassan;Adam Bates;Carl A. Gunter
随着物联网（IoT）的不断普及，在日益自动化的家庭中诊断不正确的行为变得更加困难。设备和应用程序可以以长序列的用户动作规则链接在一起，直到从可观察到的症状（例如，未锁的门）可能不可能识别远距离移除的根本原因（例如，恶意应用程序）。这是因为，目前物联网审计日志被孤立在各个设备上，因此无法用于重建复杂工作流程的因果关系。在这项工作中，我们提出了ProvThings，一个以平台为中心的方法来集中审计的物联网。ProvThings对物联网应用程序和设备API执行有效的自动化检测，以生成数据来源，从而提供对系统活动（包括恶意行为）的全面解释。我们为三星SmartThings平台制作了原型ProvThings，并对我们的方法针对26种物联网攻击的有效性进行了基准测试。通过引入选择性代码插装优化，我们在评估中证明ProvThings仅对物理物联网设备施加5%的开销，同时支持系统行为的真实的查询，并进一步考虑如何利用ProvThings来满足物联网生态系统中各种利益相关者的需求。
—As the Internet of Things (IoT) continues to proliferate, diagnosing incorrect behavior within increasingly-automated homes becomes considerably more difficult. Devices and apps may be chained together in long sequences of trigger-action rules to the point that from an observable symptom (e.g., an unlocked door) it may be impossible to identify the distantly removed root cause (e.g., a malicious app). This is because, at present, IoT audit logs are siloed on individual devices, and hence cannot be used to reconstruct the causal relationships of complex workflows. In this work, we present ProvThings, a platform-centric approach to centralized auditing in the Internet of Things. ProvThings performs efficient automated instrumentation of IoT apps and device APIs in order to generate data provenance that provides a holistic explanation of system activities, including malicious behaviors. We prototype ProvThings for the Samsung SmartThings platform, and benchmark the efficacy of our approach against a corpus of 26 IoT attacks. Through the introduction of a selective code instrumentation optimization, we demonstrate in evaluation that ProvThings imposes just 5% overhead on physical IoT devices while enabling real time querying of system behaviors, and further consider how ProvThings can be leveraged to meet the needs of a variety of stakeholders in the IoT ecosystem.