  Analyzing users' Internet traffic data and activities has a certain impact on
users' experiences in different ways, from maintaining the quality of service
on the Internet and providing users with high-quality recommendation systems to
anomaly detection and secure connection. Considering that the Internet is a
complex network, we cannot disintegrate the packets for each activity.
Therefore we have to have a model that can identify all the activities an
Internet user does in a given period of time. In this paper, we propose a deep
learning approach to generate a multi-label classifier that can predict the
websites visited by a user in a certain period. This model works by extracting
the server names appearing in chronological order in the TLSv1.2 and TLSv1.3
Client Hello packets. We compare the results on the test data with a simple
fully-connected neural network developed for the same purpose to prove that
using the time-sequential information improves the performance. For further
evaluations, we test the model on a human-made dataset and a modified dataset
to check the model's accuracy under different circumstances. Finally, our
proposed model achieved an accuracy of 95% on the test dataset and above 90% on
both the modified dataset and the human-made dataset.
