Yin Jie, Zhang Chuntang, Xie Wenwei, Liang Guangjun, Zhang Lanping, Gui Guan
Computer Information and Cyber Security, Jiangsu Police Institute, Nanjing, 210031 China.
Bell Honors School, Nanjing University of Posts and Telecommunications, Nanjing, 210023 China.
Peer Peer Netw Appl. 2023 Apr 26:1-16. doi: 10.1007/s12083-023-01482-0.
The detection of anomaly traffic in internet of things (IoT) is mainly based on the original binary data at the traffic packet level and the structured data at the session flow level. This kind of dataset has a single feature extraction method and relies on prior manual knowledge. It is easy to lose critical information during data processing, which reduces the validity and robustness of the dataset. In this paper, we first construct a new anomaly traffic dataset based on the traffic packet and session flow data in the Iot-23 dataset. Second, we propose a feature extraction method based on feature fluctuation. Our proposed method can effectively solve the disadvantage that the data collected in different scenarios have different characteristics, which leads to the feature containing less information. Compared with the traditional anomaly traffic detection model, experiments show that our proposed method based on feature fluctuation has stronger robustness, can improve the accuracy of anomaly traffic detection and the generalization ability of the traditional model, and is more conducive to the detection of anomalous traffic in IoT.
物联网（IoT）中异常流量的检测主要基于流量数据包级别的原始二进制数据和会话流级别的结构化数据。这类数据集具有单一的特征提取方法，且依赖于先验的人工知识。在数据处理过程中很容易丢失关键信息，这降低了数据集的有效性和鲁棒性。在本文中，我们首先基于Iot - 23数据集中的流量数据包和会话流数据构建了一个新的异常流量数据集。其次，我们提出了一种基于特征波动的特征提取方法。我们提出的方法能够有效解决不同场景下收集的数据具有不同特征，导致特征包含信息较少的缺点。与传统的异常流量检测模型相比，实验表明我们提出的基于特征波动的方法具有更强的鲁棒性，能够提高异常流量检测的准确率以及传统模型的泛化能力，更有利于物联网中异常流量的检测。
