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English(EN) DRIFT: Drift-Resilient Invariant-Feature Transformer for DGA Detection

新型Transformer模型DRIFT增强DGA检测能力以应对不断演变的威胁

研究人员开发了一个名为DRIFT的新型基于Transformer的框架,以对抗僵尸网络中使用的领域生成算法(DGA)不断演变的威胁。通过一项为期九年的研究,他们观察到随着新变种的出现,现有的DGA检测方法会迅速退化。DRIFT通过学习不变表示来解决这个问题,它采用混合标记化策略,结合了字符级和子词级编码,以及多任务自监督预训练。评估表明,DRIFT显著缓解了时间退化,并在前向链接实验中超越了当前最先进的基线,提供了更可靠的长期防御。 AI

影响 通过提供更强大的防御能力来应对不断演变的网络威胁,从而增强网络安全。

排序理由 该集群描述了一篇详细介绍用于DGA检测的新模型的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新型Transformer模型DRIFT增强DGA检测能力以应对不断演变的威胁

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该集群描述了一篇详细介绍用于DGA检测的新模型的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chaeyoung Lee, Chaeri Jung, Seonghoon Jeong ·

    DRIFT:用于 DGA 检测的抗漂移不变特征 Transformer

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