论文标题
使用优化的数字足迹的设备识别
Device identification using optimized digital footprints
论文作者
论文摘要
物联网（IoT）和非iot设备的迅速增加，对网络管理员构成了新的安全挑战。必须在日益复杂的网络结构中进行准确的设备识别。在本文中，已经提出了一种基于数字足迹的设备指纹（DFP）方法，用于设备标识，该数字足迹是通过网络通过网络通信的设备。已经从基于WEKA中属性评估器的单个传输控制协议/Internet协议数据包的网络和传输层中选择了九个功能的子集，以生成特定于设备的签名。该方法已在两个在线数据集和一个实验数据集上评估，并使用不同的监督机学习（ML）算法评估。结果表明，该方法能够使用随机森林（RF）分类器以多达100％的精度区分设备类型，并对具有多达95.7％精度的单个设备进行分类。这些结果证明了所提出的DFP方法在设备识别中的适用性，以提供更安全，更健壮的网络。
The rapidly increasing number of internet of things (IoT) and non-IoT devices has imposed new security challenges to network administrators. Accurate device identification in the increasingly complex network structures is necessary. In this paper, a device fingerprinting (DFP) method has been proposed for device identification, based on digital footprints, which devices use for communication over a network. A subset of nine features have been selected from the network and transport layers of a single transmission control protocol/internet protocol packet based on attribute evaluators in Weka, to generate device-specific signatures. The method has been evaluated on two online datasets, and an experimental dataset, using different supervised machine learning (ML) algorithms. Results have shown that the method is able to distinguish device type with up to 100% precision using the random forest (RF) classifier, and classify individual devices with up to 95.7% precision. These results demonstrate the applicability of the proposed DFP method for device identification, in order to provide a more secure and robust network.
