Encrypted traffic classification (ETC) plays an important role in network management. In most research, the statistical features, transformed traffic images, or text are used for classification. However, the statistical features’ design is time-consuming and labor-intensive, and the transformed traffic data lack spatial or semantic features. Considering that the headers of traffic packets have a uniform structure and are independent of each other, traffic data are most similar to tabular data. Thus we propose a data processing approach to convert packet headers into traffic tables in which each field is viewed as a column (feature). In addition, traffic data are hard to label in real traffic environments, and each field contributes differently to the classification. Therefore, a self-supervised learning algorithm, SubTab, is used as the baseline network to reduce the reliance on labeled data and assign different weights to different fields. To the best of our knowledge, this is the first time that the ETC problem is solved from the tabular domain. Experimental results on two real-world datasets, ISCX VPN-nonVPN and the self-collected dataset SHU-ET, demonstrate that our method surpasses state-of-the-art methods based on traffic images or text and proves that traffic tables are more suitable for ETC problems. In addition, our method achieves a great performance with only 10% of labeled data and reduces the reliance on labeling data.
With the constant updating of applications and the emergence of various encryption technologies, a large amount of new encrypted network traffic is generated every day. Therefore, it is a challenging task to achieve continual learning of encrypted traffic. Existing encrypted traffic classification techniques can only handle a fixed number of traffic classes, which is not applicable to real network environments. In this paper, we proposed a continual encrypted traffic classification method based on WGAN, called CETC. The method takes advantage of the powerful data generation capabilities of WGAN to model the data distribution of encrypted traffic. When learning from a new traffic class, the samples from the old class is generated by WGAN to train the new classifier. We use the ISCX VPN-nonVPN dataset to test the performance of CETC. Experimental results show that WGAN can generate high-quality samples of encrypted traffic and the accuracy of CETC is higher than 93%. With its efficient and continual learning capability, CETC can be applied to various encrypted traffic detection and management systems.
KEYWORDS: Head, Detection and tracking algorithms, Time of flight cameras, Digital filtering, Image processing, Hough transforms, Communication engineering, Video, Data conversion
The traditional people detection is mainly based on the two-dimensional data acquired from RGB images or videos. While with the help of the Time-of-Flight (TOF) camera, researchers can convert traditional two-dimensional data based on images or videos into pseudo-three-dimensional data containing depth information to achieve more accurate people detection. The research of this paper uses only the depth information and it is an important part of people counting. Based on the preprocessing of depth images, an algorithm based on Connected Component Analysis is proposed according to the characteristics of people in top-view scene. Aiming at the shortcomings of the algorithm in the crowd, the 21Hough Transform(HT) people head detection algorithm combined with depth information and priori conditions is proposed. And thus, we succeed in screening out the non-head objects and achieving real-time, accurate people detection. This study lays a solid foundation for the follow-up people counting.
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