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RAL - Reinforcement Active Learning for Network Traffic Monitoring and Analysis

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Abstract

Network-traffic data usually arrives in the form of a data stream. Online monitoring systems need to handle the incoming samples sequentially and quickly. These systems regularly need to get access to ground-truth data to understand the current state of the application they are monitoring, as well as to adapt the monitoring application itself. However, with in-the-wild network-monitoring scenarios, we often face the challenge of limited availability of such data. We introduce RAL, a novel stream-based, active-learning approach, which improves the ground-truth gathering process by dynamically selecting the most beneficial measurements, in particular for model-learning purposes.
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Dates and versions

hal-02932839 , version 1 (07-09-2020)

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Sarah Wassermann, Thibaut Cuvelier, Pedro Casas. RAL - Reinforcement Active Learning for Network Traffic Monitoring and Analysis. ACM SIGCOMM 2020 Posters, Demos, and Student Research Competition, Aug 2020, New York / Virtual, United States. ⟨10.1145/3405837.3411390⟩. ⟨hal-02932839⟩

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