Researchers have developed a new Transformer-based framework called DRIFT to combat the evolving threat of Domain Generation Algorithms (DGAs) used in botnets. Through a nine-year study, they observed that existing DGA detection methods rapidly degrade as new variants emerge. DRIFT addresses this by learning invariant representations using a hybrid tokenization strategy that combines character-level and subword-level encoding, along with multi-task self-supervised pre-training. Evaluations show DRIFT significantly mitigates temporal degradation and outperforms current state-of-the-art baselines in forward-chaining experiments, offering a more dependable long-term defense. AI
IMPACT Enhances network security by providing a more robust defense against evolving cyber threats.
RANK_REASON The cluster describes a research paper detailing a new model for DGA detection. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- DRIFT
- Gotit.pub
- Hugging Face
- ScienceCast
- Seonghoon Jeong
- Transformer
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