Publications

Researchers are in constant need of reliable data to develop and evaluate AI/ML methods for networks and cybersecurity. While Internet measurements can provide realistic data, such datasets lack ground truth about application flows. We present a ~750GB dataset that includes ~2000 systematically conducted experiments and the resulting packet captures with video streaming, video teleconferencing, and cloud-based document editing applications. This curated and labeled dataset has bidirectional and encrypted traffic with complete ground truth that can be widely used for assessments and evaluation of AI/ML algorithms.

This paper describe our team's experience using minimega, a network emulation system using node and network virtualization, to support evaluation of a set of networked and distributed systems for topology discovery, traffic classification and engineering in the DARPA Searchlight program [18]. We present the methodology we developed to encode network and traffic definitions into an experiment description model, and how our tools compile this model onto the underlying minimega API. We then present three cases studies which demonstrate the ability of our EDM to support experiments with diverse network topologies, diverse traffic mixes, and networks with specialized layer-2 connectivity requirements. We conclude with the overall takeaways from using minimega to support our evaluation process.