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相关概念视频

Detection of Black Holes01:10

Detection of Black Holes

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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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基于改进的KNN算法进行网络入侵检测.

Hongsheng Bao1, Jie Gao2

  • 1Internet of Things and Intelligent Engineering School, Jiangsu Vocational Institute of Commerce, Nanjing, 211168, China.

Scientific reports
|August 14, 2025
PubMed
概括

一个新的K-最近邻近算法改善了网络攻击检测,以超过85%的准确性对正常和探测威胁进行分类. 这种网络安全增强显著提高了整体攻击检测率.

科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 网络入侵检测 网络入侵检测

背景情况:

  • 越来越多的网络攻击,如特洛伊木马,虫和勒索软件,对网络安全和国家安全构成重大威胁.
  • 传统方法可能不足以应对不断变化的网络威胁.

研究的目的:

  • 提出一种新的三分支决策软增量K-最近邻近算法,用于增强网络攻击分类.
  • 提高检测各种类型的网络攻击的准确性和效率.

主要方法:

  • 开发了一个三分支决策软增量K-最近邻近算法,以区间集表示类集群.
  • 介绍了一个初始的K-最近邻近算法,该算法基于预集群数据集的代表点.
  • 实施预先集群,以减轻数据处理顺序对结果的影响.

主要成果:

  • 改进的K-最近邻近算法在分类正常,DoS和探测攻击方面取得了超过85%的准确性.
  • 在U2R攻击中获得了57.32%的平均准确率.
  • 提出的方法在数据集上显示了最高的分类准确性,所有攻击类型的检测率都超过了98%.

结论:

  • 拟议的算法有效地以高准确度对常见的网络攻击进行分类.
关键词:
分类 分类 分类 分类.增量学习是一种增量学习.在KNN算法中,KNN算法网络入侵 网络入侵三个分支机构的决策.

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  • 该方法在提高整体网络安全和威胁检测能力方面显示出重大前景.
  • 进一步的研究可能将重点放在改善U2R攻击检测率上.