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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Updated: May 11, 2025

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一个基于模拟的计算框架,用于基于机器学习的入侵检测系统中的适应性能源效率优化.

Ripal Ranpara1, Osamah Alsalman2, Om Prakash Kumar3

  • 1Faculty of Computer Applications, Marwadi University, Rajkot, 360003, India. ripal.ranpara@marwadieducation.edu.in.

Scientific reports
|April 18, 2025
PubMed
概括
此摘要是机器生成的。

绿色MU通过使用机器学习平衡能源效率和准确性来增强入侵检测系统. 这种新的框架显著提高了检测率,同时降低了资源有限的环境的能源消耗.

关键词:
网络安全 网络安全绿色的人工智能 绿色的人工智能侵入者检测系统 侵入者检测系统机器学习 机器学习基于模拟的优化优化智能算法是一种智能算法.

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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
  • 机器学习 机器学习
  • 绿色计算 绿色计算

背景情况:

  • 侵入检测系统 (IDS) 在平衡检测性能与能源效率方面面临着挑战.
  • 越来越复杂的网络威胁需要复杂但资源意识的安全解决方案.
  • 资源有限的环境,如物联网和边缘计算,需要节能的安全框架.

研究的目的:

  • 提出GreenMU,这是一个新的框架,用于解决入侵检测系统中的能源效率和检测性能.
  • 整合机器学习,知识蒸和适应性能源意识优化,以加强网络安全.
  • 根据能量和威胁水平开发MUGuard算法,用于基于能量和威胁水平的动态计算复杂度调整.

主要方法:

  • 随机森林和支持矢量机器分类器的整合.
  • 应用知识蒸和适应性能源意识优化技术.
  • 开发和实施MUGuard算法用于实时自适应处理.

主要成果:

  • 在KDD 1999数据集上,GreenMU实现了接近99%的检测准确度,超过了基线模型.
  • 与标准模型相比,能源消耗减少了31%.
  • 通过将处理时间缩短15%,提高了计算效率.

结论:

  • 绿色MU为现代入侵检测提供了一个可扩展,可持续和高性能解决方案.
  • 该框架有效地平衡了计算效率和网络安全准确性.
  • 强调绿色人工智能在推进资源有限环境的网络安全方面的潜力.