论文标题
部分可观测时空混沌系统的无模型预测
IoT Device Identification Based on Network Traffic Characteristics
论文作者
论文摘要
物联网设备识别在监视和改善物联网设备的性能和安全性方面起着重要作用。与传统的非iot设备相比，物联网设备在检测物联网设备的类型方面为我们提供了独特的挑战和机遇。基于我们以前关于了解IoT设备网络流量特征的关键见解，在本文中，我们开发了一种有效的基于机器学习的物联网设备识别方案，名为IotID。在开发iotid时，我们从三个互补方面提取了70个TCP流的功能：远程网络服务器和端口号，数据包级的流量特征，例如数据包间隔时间以及流量流量特征，例如流量持续时间。与现有工作不同，我们考虑了各种设备在IOTID的学习和评估阶段产生的网络流量的不平衡性质。我们基于在由物联网和非iot设备组成的典型智能家庭环境中收集的网络流量的绩效研究表明，Iotid可以达到超过99％的平衡精度得分。
IoT device identification plays an important role in monitoring and improving the performance and security of IoT devices. Compared to traditional non-IoT devices, IoT devices provide us with both unique challenges and opportunities in detecting the types of IoT devices. Based on critical insights obtained in our previous work on understanding the network traffic characteristics of IoT devices, in this paper we develop an effective machine-learning based IoT device identification scheme, named iotID. In developing iotID, we extract 70 features of TCP flows from three complementary aspects: remote network servers and port numbers, packet-level traffic characteristics such as packet inter-arrival times, and flow-level traffic characteristics such as flow duration. Different from existing work, we take into account the imbalance nature of network traffic generated by various devices in both the learning and evaluation phases of iotID. Our performance studies based on network traffic collected on a typical smart home environment consisting of both IoT and non-IoT devices show that iotID can achieve a balanced accuracy score of above 99%.
