Abstract
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.
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CITATION STYLE
Ardi, C., Aubry, C., Kocoloski, B., Deangelis, D., Hussain, A., Troglia, M., & Schwab, S. (2022). The DARPA SEARCHLIGHT Dataset of Application Network Traffic. In ACM International Conference Proceeding Series (pp. 59–64). Association for Computing Machinery. https://doi.org/10.1145/3546096.3546103
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