Security Testbed for Internet of Things

该项目侧重于物联网流量和网络安全。我们设置了物联网测试平台并收集流量；根据我们的观察，我们能够设计一个新的物联网流量生成器工具，这是帮助检测未来物联网流量异常的重要工具。我们通过流量熵观察了从IoTTGen收集的流量和生成的流量，我们证明了我们的生成器能够捕获物联网流量的基本特征。然后，我们将物联网流量异常添加到我们的数据集中。我们观察了生成数据集和合成数据集对交通熵的影响。因此，我们的工具可以准确地模拟物联网流量并帮助检测物联网异常。基于这一观察，我们提出了一个基于机器学习算法的新框架，根据设备的流量概况来识别设备。我们的框架允许非常精确地检测智能家居物联网设备的性质，并且还能够区分异常设备。事实上，我们的结果显示，超过90%的设备被正确识别，在网络安全威胁的情况下也是如此。这是识别合法物联网流量模式与异常流量模式的重要一步。

This project focuses on IoT Traffic and cybersecurity. We set an IoT Testbed and collected traffic; based on our observations, we were able to design a new IoT Traffic generator tool, which is an essential tool to help detecting anomalies for future IoT Traffic.We observed the collected traffic and the generated traffic from IoTTGen through traffic entropy and we showed that our generator is able to capture the essential characteristics of IoT Traffic. We then add IoT traffic anomaly into our data set. We observe the impact on the traffic entropy on both generated and synthetic data set. Our Tool is therefore accurate to model IoT traffic and help detecting IoT anomaly.Based on this observation we propose a new framework based on Machine Learning algorithms to identify devices based on their traffic profile. Our framework allows detecting very precisely the nature of smart home IoT devices and is also able to distinguish the devices with anomalies. Indeed, our results showed that more than 90% of devices were properly identified and also in the case of cybersecurity threats. It is an important step toward the recognition of legitimate IoT traffic pattern with regards to anomalous ones.