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相关实验视频

Updated: Jun 11, 2025

In Vitro Selection of Aptamers to Differentiate Infectious from Non-Infectious Viruses
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一种基于先进计算的APT攻击检测的新方法.

Cho Do Xuan1, Tung Thanh Nguyen2

  • 1Faculty of Information Security, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam. chodx@ptit.edu.vn.

Scientific reports
|September 27, 2024
PubMed
概括
此摘要是机器生成的。

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本研究介绍了一种通过构建和分析网络流量行为概况来检测高级持久威胁 (APT) 的新方法. 拟议的BiADG模型显著提高了APT攻击预测的准确性,超过了现有的方法.

科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 高级持久威胁 (APT) 对网络安全构成重大风险.
  • 现有的APT检测方法经常在准确性和错误预测方面扎.
  • 有效的行为分析对于识别复杂的网络攻击至关重要.

研究的目的:

  • 提出一种新的方法来增强高级持久威胁 (APT) 检测.
  • 在网络流量中构建和分析APT攻击的行为概况.
  • 为了提高APT检测的准确性和减少错误预测.

主要方法:

  • 利用双向长期短期记忆 (Bi) 和注意力 (A) 的组合来构建行为概况.
  • 采用动态图卷积神经网络 (DGCNN) 进行特征提取和APT的分类.
  • 开发了BiADG模型,将这些组件集成为全面的APT分析.

主要成果:

  • 与现有方法相比,拟议的BiADG模型在APT检测方面表现出优异的性能.
  • 实现了APT攻击预测的准确率在84%至91%之间,提高了7%以上.
  • 该模型有效地识别了与APT攻击相关的重要信息和行为.
关键词:
攻击检测检测APT攻击检测APT攻击检测注意力 注意力 注意力 注意力这就是BiLSTM.动态图形卷积神经网络的神经网络.

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结论:

  • BiADG模型是用于在网络流量中检测APT的适当和有效方法.
  • 研究验证了拟议方法的优越性和有效性.
  • 这项工作为检测DDoS,尸网络和恶意软件等其他网络攻击开辟了新的途径.