Abstract
Internet of Things (IoT) device identification is a key step in the management of IoT devices. The devices connected to the network must be controlled by the manager. For this purpose, many schemes are proposed to identify IoT devices, especially the schemes working on the gateway. However, almost all researchers do not pay close attention to the cost. Thus, considering the gateway’s limited storage and computational resources, a new lightweight IoT device identification scheme is proposed. First, the DFI (deep/dynamic flow inspection) technology is utilized to efficiently extract flow-related statistical features based on in-depth studies. Then, combined with symmetric uncertainty and correlation coefficient, we proposed a novel filter feature selection method based on NSGA-III to select effective features for IoT device identification. We evaluate our proposed method by using a real smart home IoT data set and three different ML algorithms. The experimental results showed that our proposed method is lightweight and the feature selection algorithm is also effective, only using 6 features can achieve 99.5% accuracy with a 3-minute time interval.
Highlights
With the popularization and development of high-speed networks, artificial intelligence, big data, and other technologies, the number of IoT (Internet of ings) devices connected to the Internet has rapidly increased
(3) Based on NSGA-III, we introduce symmetric uncertainty and correlation coefficient and propose a novel low-overhead feature selection method to perform feature selection on the extracted flow-related statistical features in IoT device identification, and the valid features are filtered while reducing the dimensionality of the features
We take the captured traffic as input and select a fixed time interval to split the traffic; second, we generate flows from the split traffic, extract flow-level features by a statistical method, and filter out invalid and redundancy features by the proposed feature selection method, which is based on NSGA-III; a variety of machine learning algorithms and the features selected in the previous step are integrated to classify devices and multiple time intervals are selected for experimentation. e most suitable time interval and machine learning algorithm is selected to build the efficient device classification model
Summary
With the popularization and development of high-speed networks, artificial intelligence, big data, and other technologies, the number of IoT (Internet of ings) devices connected to the Internet has rapidly increased. To better identify devices on the gateway, this study proposed a lightweight IoT device identification method based on flow features. Is solution studies the flow-related statistical characteristics intensively; to pursue less cost, a novel NSGA-III-based [7, 8] filter type feature selection algorithm is proposed; and the extra random tree algorithm is used to build a device recognition model to Security and Communication Networks classify devices. (3) Based on NSGA-III, we introduce symmetric uncertainty and correlation coefficient and propose a novel low-overhead feature selection method to perform feature selection on the extracted flow-related statistical features in IoT device identification, and the valid features are filtered while reducing the dimensionality of the features.
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