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相关概念视频

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Machines: Problem Solving I01:22

Machines: Problem Solving I

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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相关实验视频

基于机器学习的物联网网络安全解决方案:全面调查

Abdullah Alfahaid1, Easa Alalwany1, Abdulqader M Almars1

  • 1Department of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 通过检测威胁和减轻风险来增强物联网 (IoT) 的安全性. 本调查审查了基于机器学习的解决方案 (2020-2024年),确定了强大的物联网网络安全的局限性和未来研究.

关键词:
物联网安全物联网安全物联网安全敌对的攻击是对抗性的攻击.检测异常检测异常检测网络安全 网络安全深度学习 (DL) 是指深度学习.联合学习 (FL)物联网 (IoT) 的物联网 (IoT) 的物联网.侵入检测系统 (IDS) 是一种入侵检测系统.机器学习 (ML) 是指机器学习.隐私保护 隐私保护 隐私保护

相关实验视频

科学领域:

  • 网络安全和网络工程 网络安全和网络工程
  • 人工智能和机器学习

背景情况:

  • 物联网 (IoT) 提供了跨行业的变革潜力,但面临着重大安全挑战,包括数据泄露,隐私侵犯和网络威胁.
  • 有效的安全机制对于物联网采用至关重要,机器学习 (ML) 显示出异常和入侵检测的希望.

研究的目的:

  • 为2020年至2024年期间实施的基于机器学习的物联网 (IoT) 安全解决方案提供全面的审查.
  • 系统地分类物联网安全的ML技术,分析威胁分类法,并评估当前解决方案的有效性,可扩展性和隐私保护.

主要方法:

  • 对最近的文献 (2020-2024) 的系统调查,重点是物联网安全中的机器学习应用.
  • 检查监督,无监督和强化学习,以及先进的技术,如深度学习 (DL),集体学习 (EL),联合学习 (FL) 和转移学习 (TL).
  • 根据应用,威胁分类和现有解决方案的批判性评估对ML技术的分类.

主要成果:

  • 包括DL,EL,FL和TL在内的ML显示了通过异常和入侵检测来提高物联网安全性的巨大潜力.
  • 目前的ML解决方案面临诸如高计算成本,对抗性攻击的脆弱性和解释性挑战等局限性.
  • 开发了物联网安全威胁的分类和安全应用的ML技术的分类.

结论:

  • 机器学习是推进物联网安全的关键工具,提供适应性和智能威胁缓解策略.
  • 未来的研究应该集中在保护隐私的ML,可解释的AI (XAI) 和基于边缘的安全框架上,以解决当前的局限性.
  • 开发强大,智能和适应性网络安全模型对于保护未来的物联网生态系统免受不断变化的威胁至关重要.