SyntIoT:面向消费者的物联网生态系统中的隐私和安全实验

Tomasz Kosinski, R. Scandariato, M. Fjeld
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引用次数: 0

摘要

自从智能家居等面向消费者的物联网产品出现以来,研究人员已经接受了保护消费者免受这项技术带来的风险(包括隐私损害)的挑战。然而,安全和隐私研究“渴望”开放数据(例如,关于设备的网络流量模式)和开放平台,以在纯模拟环境之外验证物联网相关解决方案。除了在相关工作中看到的少数情况外,数据集不容易提供给研究界,并且很难在内部生产。此外,由于缺乏开放的实验平台(用于测试隐私和安全解决方案),研究结果和开放科学的可重复性受到阻碍,该平台还提供了对实验设置的细粒度控制。我们推出了SyntIoT,这是一个平台,允许研究人员以低成本轻松地将完整的物联网生态系统(包括设备,用户,供应商云)部署到物理世界,从而降低了进入该研究领域的门槛。SyntIoT可用于收集现场数据,并实际验证安全和隐私解决方案。我们的平台使用合成物联网设备,这些设备以声明的方式完全可配置。有趣的是,我们的平台还允许商用设备与合成设备一起部署。该平台提供了一个基础设施来监控生态系统并提取丰富的数据,这些数据可用于实证研究和数据挖掘。本文介绍了该平台,解释了它如何满足之前作品中尚未回答的既定研究需求,并强调了它在三种实验场景中的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SyntIoT: Privacy and security experimentation in consumer-oriented IoT ecosystems
Since the advent of consumer-oriented IoT products, like smart homes, researchers have taken up the challenge of shielding the consumers from the risks this technology entails, including privacy harms. However, security and privacy research is ‘hungry’ for open data (e.g., about the network traffic patterns of the devices) and open platforms to validate IoT-related solutions outside a pure simulation environment. Except for the few cases seen in the related work, datasets are not readily available to the research community and are difficult to produce in-house. Also, the reproducibility of research results and open science is hindered by the lack of an open experimentation platform (to test privacy and security solutions) that also offers a fine-grained control of the experimental setup. We present SyntIoT, a platform that allows researchers to easily deploy a complete IoT ecosystem (including devices, users, vendor clouds) into the physical world and at a low cost, hence lowering the barriers to entry in this research field. SyntIoT can be used to collect field data and to realistically validate security and privacy solutions. Our platform uses synthetic IoT devices that are fully configurable in a declarative way. Interestingly, our platform also allows commercial devices to be deployed alongside the synthetic ones. The platform provides an infrastructure to monitor the ecosystem and to extract rich data, which can be used for empirical research and data mining. This paper presents the platform, explains how it meets established research needs not yet answered in previous works, and highlights its usage in the context of three experimental scenarios.
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