Device Fingerprinting for Access Control over a Campus and Isolated Network
收藏资源简介：
Device Fingerprinting for Access Control over a Campus and Isolated Network Device Fingerprinting (DFP) is a technique to identify devices using Inter-Arrival Time (IAT) of packets and without using any other unique identifier. Our experiments include generating graphs of IATs of 100 packets and using Convolutional Neural Network on the generated graphs to identify a device. We did two experiments where the first experiment was on Raspberri Pi and other experiment was on crawdad dataset. First Experiment: Raspberry Pi We developed a packet sniffer application to capture IAT of packets. Packet sniffer application was installed on Raspberry pi that was configured to work as router. We connceted two devices iPad4 and iPhone 7 Plus to the router and created IAT graphs for these two devices. Our scheme based on Convolution Neural Network (CNN) was able to identify the devices with accuracy of 86.7%. Second Experiment: Crawdad Dataset In the second experiment, we tested the scheme with Crawdad dataset. The proposed scheme achieved accuracy of 95.5% for GTID that is 3% higher than previous scheme for 14 devices and 5 device types on isolated network while 40% efficient in time to test a device fingerprint.
面向园区与隔离网络访问控制的设备指纹识别 设备指纹识别（Device Fingerprinting, DFP）是一种仅利用数据包到达间隔时间（Inter-Arrival Time, IAT）即可识别设备，且无需借助其他唯一标识符的技术。本实验方案包含生成100个数据包的IAT图谱，并在生成的图谱上应用卷积神经网络以完成设备识别。我们共开展两组实验：第一组实验基于树莓派（Raspberry Pi）平台，第二组实验采用Crawdad数据集。 第一组实验：树莓派平台实验 我们开发了一款数据包嗅探应用程序以捕获数据包的IAT值。该应用被部署在配置为路由器模式的树莓派上。我们将iPad4与iPhone 7 Plus两台设备连接至该路由器，并为这两台设备生成对应的IAT图谱。基于卷积神经网络（Convolutional Neural Network, CNN）的本方案可实现86.7%的设备识别准确率。 第二组实验：Crawdad数据集实验 在第二组实验中，我们采用Crawdad数据集对所提出的方案进行性能验证。针对GTID场景，本方案的识别准确率达到95.5%；在隔离网络环境下的14台设备与5种设备类型的测试场景中，该准确率较此前同类方案高出3%；同时，单台设备指纹的测试耗时较此前方案缩短40%，时间效率提升显著。
