论文标题
检测和分类IoT相机流量
Detect and Classify IoT Camera Traffic
论文作者
论文摘要
在组织中的IoT摄像机的部署威胁安全和隐私政策，并且不使用IP地址和端口号的网络流量对网络流量进行分类是具有挑战性的。在本文中，我们设计，实施和部署了一个名为ICAMINSPECTOR的系统，以对混合网络环境中的IoT相机产生的网络流量进行分类。我们总共收集了大约36GB的网络流量，其中包含来自三种不同类型的应用程序（四种在线音频/视频会议应用程序，两个视频共享应用程序和来自不同制造商的六个IoT摄像机）的视频数据。我们显示，借助有限的基于流量的功能，Icaminspector在10倍的交叉验证中的平均准确性超过98％，在系统的测试阶段的错误速率约为1.5％。在看不见的环境中，我们的系统的真正部署实现了值得称赞的表现，即检测物联网相机的平均检测概率高于0.9。
Deployment of IoT cameras in an organization threatens security and privacy policies, and the classification of network traffic without using IP addresses and port numbers has been challenging. In this paper, we have designed, implemented and deployed a system called iCamInspector to classify network traffic arising from IoT camera in a mixed networking environment. We have collected a total of about 36GB of network traffic containing video data from three different types of applications (four online audio/video conferencing applications, two video sharing applications and six IoT camera from different manufacturers) in our IoT laboratory. We show that with the help of a limited number of flow-based features, iCamInspector achieves an average accuracy of more than 98% in a 10-fold cross-validation with a false rate of about 1.5% in testing phase of the system. A real deployment of our system in an unseen environment achieves a commendable performance of detecting IoT camera with an average detection probability higher than 0.9.
