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相关概念视频

Vector Algebra: Graphical Method01:10

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
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Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
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An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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相关实验视频

Updated: Jan 16, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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使用图形卷积网络和卷积2D KG嵌入的网络攻击知识推断.

Weiwu Ren1, Hewen Zhang1, Ying Lei2

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130012, Jilin, China.

Scientific reports
|October 3, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了KGConvE,这是一种新的图形卷积神经网络方法,用于推断隐性网络攻击知识. 它通过有效关联漏洞 (CVE),弱点 (CWE) 和攻击模式 (CAPEC) 来增强复杂网络攻击的分析.

关键词:
攻击推断的推断攻击.图表卷积神经网络的图.完成知识图表的完成.安全知识图表安全知识图表.

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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
  • 网络安全 网络安全

背景情况:

  • 分析具有复杂的多关系和多跳路径的大规模透攻击存在重大挑战.
  • 现有的方法在有效的关联挖掘中扎,挖掘隐含的网络攻击知识.

研究的目的:

  • 提出KGConvE,一种基于图形卷积神经网络的方法,用于智能推理和关联挖掘隐性网络攻击知识.
  • 为了提高攻击分类和推断任务的准确性和概括能力.

主要方法:

  • 获得的知识嵌入用于常见漏洞和暴露 (CVE),常见弱点列表 (CWE) 和常见攻击模式列表和分类 (CAPEC).
  • 构建攻击上下文特征数据和使用这些嵌入的关系矩阵.
  • 采用图形卷积神经网络 (GCN) 进行攻击分类,并使用KGConvE模型进行攻击推断.

主要成果:

  • 通过GCN改进,在攻击分类中显著提高了准确性和概括能力.
  • 成功推断出CVE-CVE,CVE-CWE和CVE-CAPEC之间的隐性关系.
  • 在网络攻击知识推断任务中获得了0.68的平均互惠等级 (MRR) 和0.58的Hits@10 ,表现优于基线方法.

结论:

  • KGConvE有效地推断了网络攻击知识中的隐含关系.
  • 拟议的方法为网络攻击知识推断提供了显著的性能改进.
  • 这项研究是首次将KGConvE模型应用于攻击推断任务.