Identifying threats contained within encrypted network traffic poses a unique set of
challenges. It is important to monitor this traffic for threats and malware, but do so in a way …

The application of machine learning for the detection of malicious network traffic has been
well researched over the past several decades; it is particularly appealing when the traffic is …

In recent years there has been a dramatic increase in the number of malware attacks that
use encrypted HTTP traffic for self-propagation or communication. Antivirus software and …

The use of TLS by malware poses new challenges to network threat detection because
traditional pattern-matching techniques can no longer be applied to its messages. However …

Anti-malware vendors receive daily thousands of potentially malicious binaries to analyse
and categorise before deploying the appropriate defence measure. Considering the …

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 …

As people's demand for personal privacy and data security becomes a priority, encrypted
traffic has become mainstream in the cyber world. However, traffic encryption is also …

This paper presents a framework for evaluating the transport layer feature space of malware
heartbeat traffic. We utilize these features in a prototype detection system to distinguish …

Robust network security systems are essential to prevent and mitigate the harming effects of
the ever-growing occurrence of network attacks. In recent years, machine learning-based …

Identifying encrypted application traffic represents an important issue for many network tasks
including quality of service, firewall enforcement and security. Solutions should ideally be …