Novel HTTPS classifier driven by packet bursts, flows, and machine learning

Encryption of network traffic recently starts to cover remaining readable information, which is heavily used by current monitoring systems; thus, it is time to focus on novel methods of encrypted traffic analysis and classification. The aim of this paper is to define a new network traffic characteristic called Sequence of packet Burst Length and Time (SBLT), which was inspired by existing approaches and definitions. Contrary to other works, SBLT is feasible even for high-speed backbone networks as a part of IP flow data. The advantage of SBLT features is shown using a machine learning classification model for HTTPS traffic types as an example. This paper presents the definition of SBLT, proposes a new annotated public dataset of HTTPS traffic with 5 categories, and evaluates the developed classifier reaching accuracy over 99 %. This classifier can help analysts to deal with a huge amount of encrypted traffic and maintain situational awareness.