As modern network communication moves closer to being fully encrypted and hence less
exposed to passive monitoring, traditional network measurements that rely on unencrypted …

While end-to-end encryption brings security and privacy to the end-users, it makes legacy
solutions such as Deep Packet Inspection ineffective. Despite the recent work in machine …

When employing supervised machine learning to analyze network traffic, the heart of the
task often lies in developing effective features for the ML to leverage. We develop GGFAST …

The security and privacy of the end-users are a few of the most important components of a
communication network. Though end-to-end encryption (eg, TLS/SSL) fulfils this …

In many scenarios like wireless Internet access or encrypted VPN tunnels, encryption is
performed on a per-packet basis. While this encryption approach effectively protects the …

Several security applications rely on monitoring network traffic, which is increasingly
becoming encrypted. In this work, we propose a pattern language to describe packet trains …

The popularity of Deep Learning (DL), coupled with network traffic visibility reduction due to
the increased adoption of HTTPS, QUIC, and DNS-SEC, re-ignited interest towards Traffic …

Traffic analysis is the process of monitoring network activities, discovering specific patterns,
and gleaning valuable information from network traffic. It can be applied in various fields …

Near ubiquitous encryption poses a challenge for security and quality of service (QoS)
applications that rely on deep packet inspection (DPI) techniques for categorizing traffic …

With the constant updating of applications and the emergence of various encryption
technologies, it is important to achieve continuous learning of encrypted traffic. Traditional …