  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 in computer networks. These
methods, neural networks in particular, are often complex and require a huge
corpus of training data. Therefore, this paper focuses on collecting a large
up-to-date dataset with almost 200 fine-grained service labels and 140 million
network flows extended with packet-level metadata. The number of flows is three
orders of magnitude higher than in other existing public labeled datasets of
encrypted traffic. The number of service labels, which is important to make the
problem hard and realistic, is four times higher than in the public dataset
with the most class labels. The published dataset is intended as a benchmark
for identifying services in encrypted traffic. Service identification can be
further extended with the task of "rejecting" unknown services, i.e., the
traffic not seen during the training phase. Neural networks offer superior
performance for tackling this more challenging problem. To showcase the
dataset's usefulness, we implemented a neural network with a multi-modal
architecture, which is the state-of-the-art approach, and achieved 97.04%
classification accuracy and detected 91.94% of unknown services with 5% false
positive rate.
