The Network Operations and Internet Security Lab at the University of Chicago, led by Neubauer Professor Nick Feamster, develops data-driven systems that derive insights from network traffic. We also explore how network traffic can reveal insights into human behavior. These insights can help us understand and improve network performance, security, and privacy, improve Internet access and affordability, and explore how the proliferation of networked devices can improve social outcomes. Read more about our projects below, or check out recent news.
We apply machine learning and statistical inference to network traffic and other environmental signals in networked environments to (1) infer and improve network performance; (2) infer human behavior and activities in a wide range of circumstances (e.g., health, education); and (3) network privacy and security.
We apply systems-driven network measurement and inference techniques to measure network performance, application quality, and user quality of experience (QoE).
We apply machine learning and inference techniques to derive information about human activity from network traffic and other environmental signals.
We apply machine learning to understand and mitigate security and privacy threats from our increasingly connected world.
We develop techniques for improving network inference, including the exploration of data representation for network modeling problems, efficient models, and operational models for network analytics.
We explore efficient representations for network traffic for both supervised and unsupervised learning problems.
We develop systems and models to enable operational network analytics, including cost-sensitive models and model drift.
We publish papers in top-tier networking, security, and machine learning/modeling conferences, also also regularly publish open-source software. An important value of our lab is real-world impact, through the deployment of operational systems. Contact us to learn more about the group, including how to join.