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As modern network communication moves closer to being fully encrypted and hence less exposed to passive monitoring, traditional network measurements that rely on unencrypted fields in captured traffic provide less and less visibility into today’s network traffic. At the same time, approaches that use techniques from machine learning (ML) to extract subtle temporal and spatial patterns from encrypted packet-level traces have shown great promise in offsetting the lack of visibility due to encryption [1–3, 5–7, 10–15, 18, 23, 24]. Despite their promise, ML-based approaches often have a credibility problem that arises from the quality of underlying training data. Given the challenges of curating high-quality training data at scale, researchers typically end up collecting their own (or reusing existing third-party or synthetic) data, often from small-scale testbeds. Such data is generally of low quality as it is not representative of …