USTC-TFC2016

The USTC-TFC2016 dataset is mainly used for network traffic classification research, including malicious traffic and normal application traffic, and is jointly completed by the University of Science and Technology of China and the Institute of Acoustics of the Chinese Academy of Sciences. The data comes from two sources: one is 10 types of malicious traffic selected from the CTU dataset, which were collected by researchers from the Czech CTU University from real environments between 2011 and 2015; The second type is the 10 normal application traffic generated by network instrument simulation. This dataset consists of 20 types of traffic, corresponding to 20 data files, all in pcap format. In order to save space, some pcap files are compressed and uploaded. After decompression, the total size of each pcap file is 3.71GB. For more information about this dataset, please refer to: 1) Wei Wang， Ming Zhu， Xuewen Zeng， Xiaozhou Ye and Yiqiang Sheng， “Malware traffic classification using convolutional neural network for representation learning”ICOIN 2017，pp712-717; 2) Wang Wei, Research on Network Traffic Classification and Anomaly Detection Methods Based on Deep Learning, Ph.D. Thesis, University of Science and Technology of China, 2018. This dataset and preprocessing tool were released in 2018 https://github.com/echowei/ Many domestic and foreign researchers are using this dataset. Due to bandwidth and capacity constraints, it is often unable to download. Upload it to the "Science Database" website of the Chinese Academy of Sciences for long-term storage and easy download. At the same time, we look forward to relevant researchers uploading and sharing new malicious traffic and encrypted traffic using domestic cryptographic protocols, as well as expanding this dataset to include more types of malicious and normal traffic (such as 100 each), forming a richer and more comprehensive dataset to approach the actual network traffic situation.