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使用RoBERTa的大型和多源网络威胁情报的元数据驱动的恶意URL检测.

Lina Chen1, Liang Meng2

  • 1Guangxi Power Grid Co. Ltd., Nanning, 530022, China.

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概括
此摘要是机器生成的。

这项研究引入了一种使用RoBERTa-Large变压器进行高级恶意URL检测的新方法,达到98%的准确性. 这种方法在识别网络鱼和恶意软件威胁方面明显优于传统的机器学习和深度学习模型.

关键词:
人工智能的人工智能是人工智能.网络安全 网络安全深度学习是一种深度学习.恶意URL检测 恶意URL检测罗伯特 罗伯特是一个人.变压器 变压器 变压器

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科学领域:

  • 网络安全 网络安全
  • 人工智能的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 恶意URL是网络攻击的主要载体,如网络鱼和恶意软件分发.
  • 现有的机器学习 (ML) 和深度学习 (DL) 模型与新的对抗性操纵作斗争.
  • 序列模型捕获了字符级别的模式,但缺乏对复杂威胁的强度.

研究的目的:

  • 开发一种强大而准确的检测恶意URL的方法.
  • 为了提高安全性,利用最先进的大型语言模型.
  • 为了提高恶意URL检测模型的解释性.

主要方法:

  • 应用RoBERTa-Large变压器,一个双机制的大型语言模型.
  • 整合上下文化子词嵌入与元数据信号通过注意层.
  • 微调模型在一个平衡的数据集上的良性,破坏性,网络鱼和恶意软件URL.

主要成果:

  • 在恶意URL检测中实现了98%的整体准确性.
  • 显著超过现有的ML和DL模型的性能.
  • SHAP和LIME分析证实了关键特征 (URL长度,斜深度,) 并通过注意力头识别了微妙的词汇异常.

结论:

  • 将元数据注意力与掩盖语言模型集成,为恶意URL检测提供了最先进的性能.
  • 拟议的方法为现实应用提供了透明的决策.
  • 罗伯塔大型变压器在网络安全威胁检测方面取得了重大进展.