The development of the Internet has led to the complexity of network encrypted traffic.
Identifying the specific classes of network encryption traffic is an important part of …

The promise of Deep Learning (DL) in solving hard problems such as network Traffic
Classification (TC) is being held back by the severe lack of transparency and explainability …

The recent success and proliferation of machine learning and deep learning have provided
powerful tools, which are also utilized for encrypted traffic analysis, classification, and threat …

Deep learning models have shown to achieve high performance in encrypted traffic
classification. However, when it comes to production use, multiple factors challenge the …

Datasets of labeled network traces are essential for a multitude of machine learning (ML)
tasks in networking, yet their availability is hindered by privacy and maintenance concerns …

The COVID-19 pandemic has reshaped Internet traffic due to the huge modifications
imposed to lifestyle of people resorting more and more to collaboration and communication …

With the increasing prevalence of encrypted network traffic, cybersecurity analysts have
been turning to machine learning (ML) techniques to elucidate the traffic on their networks …

The understanding of realistic censorship threats enables the development of more resilient
censorship circumvention systems, which are vitally important for advancing human rights …

Deep learning (DL) has been successfully applied to encrypted network traffic classification
in experimental settings. However, in production use, it has been shown that a DL classifier's …

Over the last years we witnessed a renewed interest toward Traffic Classification (TC)
captivated by the rise of Deep Learning (DL). Yet, the vast majority of TC literature lacks …