  Monitoring network traffic to identify content, services, and applications is
an active research topic in network traffic control systems. While modern
firewalls provide the capability to decrypt packets, this is not appealing for
privacy advocates. Hence, identifying any information from encrypted traffic is
a challenging task. Nonetheless, previous work has identified machine learning
methods that may enable application and service identification. The process
involves high level feature extraction from network packet data then training a
robust machine learning classifier for traffic identification. We propose a
classification technique using an ensemble of deep learning architectures on
packet, payload, and inter-arrival time sequences. To our knowledge, this is
the first time such deep learning architectures have been applied to the Server
Name Indication (SNI) classification problem. Our ensemble model beats the
state of the art machine learning methods and our up-to-date model can be found
on github: \url{https://github.com/niloofarbayat/NetworkClassification}
