Blog
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Detecting Changes in Encrypted Traffic with AI: Introducing MFWDD
In the rapidly evolving world of network traffic monitoring, machine learning (ML) has become a cornerstone for classification tasks. But as network protocols evolve and data patterns shift, maintaining the reliability of ML models is a constant challenge. Our research team at the Czech Technical University in Prague, together with CESNET, developed a solution to tackle these challenges head-on: Model-based Feature Weight Drift Detection (MFWDD).