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 …

Recently, much attention has been devoted to the development of generative network traces
and their potential use in supplementing real-world data for a variety of data-driven …

Network data analytics are now at the core of almost every networking solution.
Nonetheless, limited access to networking data has been an enduring challenge due to …

Analysis techniques are critical for gaining insight into network traffic given both the higher
proportion of encrypted traffic and increasing data rates. Unfortunately, the domain of …

We present a processing pipeline for flow-based traffic classification using a machine
learning component leveraging Deep Neural Networks (DNNs). The system is trained to …

Many novel problems in computer networking require relevant network trace data during the
research process. Unfortunately, such data can often be hard to find, which becomes a …

Generalizing machine learning (ML) models for network traffic dynamics tends to be
considered a lost cause. Hence for every new task, we design new models and train them …

Nowadays, large volumes of data and measurements are being continuously generated by
computer and telecommunication networks, but such volumes make it difficult to extract …

The rapid progress of urbanization has expedited the process of urban planning, eg, new
residential, commercial areas, which in turn boosts the local travel demand. We propose a …

We propose a novel methodology to generate realistic network flow traces to enable
systematic evaluation of network monitoring systems in various traffic conditions. Our …