Please refer to the original article for further data description: Jan Luxemburk et al. Fine-grained TLS services classification with reject option, Computer Networks, 2023, 109467, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2022.109467 We recommend using the CESNET DataZoo python library, which facilitates the work with large network traffic datasets. More information about the DataZoo project can be found in the GitHub repository https://github.com/CESNET/cesnet-datazoo. The recent success and proliferation of machine learning and deep learning have provided powerful tools, which are also utilized for encrypted traffic analysis, classification, and threat detection. These methods, neural networks in particular, are often complex and require a huge corpus of training data. Moreover, because most of the network traffic is being encrypted, the traditional deep-packet-inspecting (DPI) solutions are becoming obsolete, and there is an urgent need for modern classification methods capable of analyzing encrypted traffic. These methods have to forgo the packet's opaque payload and focus on flow statistics and packet metadata sequences like packet sizes, directions, and inter-arrival times. The classification can be further extended with the task of "rejecting" unknown traffic, i.e., the traffic not seen during the training phase. This makes the problem more challenging, and neural networks offer superior performance for tackling this problem. When the factors of (1) the hardness of classification of encrypted traffic with unknown traffic detection and (2) the neural networks' inherent need for large datasets are combined, the requirement for a rich, large, and up-to-date dataset is even stronger. Therefore, we created a large dataset spanning two weeks, consisting of 141 million network flows, and having 191 fine-grained service labels. The dataset is intended as a benchmark for the task of identification of services in encrypted traffic with the detection of unknown services. Data capture The data was captured in the flow monitoring infrastructure of the CESNET2 network. The capturing was done for two weeks between 4.10.2021 and 17.10.2021. The following table provides per-week flow count, capture period, and uncompressed size: W-2021-40 Uncompressed Size: 22 GB Capture Period: 4.10.2021 - 10.10.2021 Flows: 73.2M W-2021-41 Uncompressed Size: 20 GB Capture Period: 11.10.2021 - 17.10.2021 Flows: 68.5M CESNET-TLS22 Uncompressed Size: 42 GB Capture Period: 4.10.2021 - 17.10.2021 Flows: 141.7M Dataset structure The dataset flows are delivered in compressed CSV files, which contain one flow per row. For each flow data file, there is a JSON file with the number of saved flows per service. There is also the stats-week.json file aggregating flow counts of a whole week and the stats-dataset.json file aggregating flow counts for the entire dataset. The mapping between services and service providers is provided in the servicemap.csv file, which also includes SNI domains used for ground truth labeling. The following table describes flow data fields in CSV files: ID: Unique identifier BYTES: Number of transmitted bytes from client to server BYTES_REV: Number of transmitted bytes from server to client PACKETS: Number of packets transmitted from client to server PACKETS_REV: Number of packets transmitted from server to client DURATION: Duration of the flow in seconds PPI: Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]] PPI_LEN: Number of packets in the PPI sequence PPI_DURATION: Duration of the PPI sequence in seconds PPI_ROUNDTRIPS: Number of roundtrips in the PPI sequence APP: Web service label CATEGORY: Service category TCP_FLAGS: TCP flags sent from client to server TCP_FLAGS_REV: TCP flags sent from server to client FLAG_CWR: Presence of the CWR flag FLAG_CWR_REV: Presence of the CWR flag in the reverse direction FLAG_ECE: Presence of the ECE flag FLAG_ECE_REV: Presence of the ECE flag in the reverse direction FLAG_URG: Presence of the URG flag FLAG_URG_REV: Presence of the URG flag in the reverse direction FLAG_ACK: Presence of the ACK flag FLAG_ACK_REV: Presence of the ACK flag in the reverse direction FLAG_PSH: Presence of the PSH flag FLAG_PSH_REV: Presence of the PSH flag in the reverse direction FLAG_RST: Presence of the RST flag FLAG_RST_REV: Presence of the RST flag in the reverse direction FLAG_SYN: Presence of the SYN flag FLAG_SYN_REV: Presence of the SYN flag in the reverse direction FLAG_FIN: Presence of the FIN flag FLAG_FIN_REV: Presence of the FIN flag in the reverse direction Link to other CESNET datasets https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/ https://github.com/CESNET/cesnet-datazoo Please cite the original article: @article{luxemburk_fine-grained-tls_2023, author = {Jan Luxemburk and Tomáš Čejka}, title = {Fine-grained TLS services classification with reject option}, journal = {Computer Networks}, volume = {220}, pages = {109467}, year = {2023}, issn = {1389-1286}, doi = {https://doi.org/10.1016/j.comnet.2022.109467}, url = {https://www.sciencedirect.com/science/article/pii/S1389128622005011} }

The creation of the dataset was supported by the Ministry of the Interior of the Czech Republic, grant No. VJ02010024: "Flow-Based Encrypted Traffic Analysis," and also by the Grant Agency of the Czech Technical University in Prague, grant No. SGS20/210/OHK3/3T/18. Computational resources were supplied by the project "e-Infrastruktura CZ" (e-INFRA CZ LM2018140) supported by the Ministry of Education, Youth and Sports of the Czech Republic.