DRIFT uses hybrid character and subword tokenization plus multi-task self-supervised pre-training to build DGA detectors that resist temporal drift and outperform baselines in forward-chaining evaluations over nine years of data.
Analyzing the real-world applicability of DGA classifiers
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DRIFT: Drift-Resilient Invariant-Feature Transformer for DGA Detection
DRIFT uses hybrid character and subword tokenization plus multi-task self-supervised pre-training to build DGA detectors that resist temporal drift and outperform baselines in forward-chaining evaluations over nine years of data.