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相关概念视频

Defenses Against Pathogens and Herbivores02:26

Defenses Against Pathogens and Herbivores

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Plants present a rich source of nutrients for many organisms, making it a target for herbivores and infectious agents. Plants, though lacking a proper immune system, have developed an array of constitutive and inducible defenses to fend off these attacks.
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Defense Mechanism Against Infection01:26

Defense Mechanism Against Infection

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Natural flora, body system defenses, and inflammation are natural barriers of the body against infectious agents regardless of previous exposure. Normal floras of the human body refer to the microbial population that colonizes the skin and mucous membranes.
In addition, many body organ systems have unique defenses against infection. The skin is an intact, multilayered surface preventing invasion by microorganisms unless impaired. Mucous membranes lining the mouth, nose, and eyelids are barriers...
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Prevention of Further Absorption of Poison01:14

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In cases of acute poisoning, the primary objective is to prevent further absorption of the toxic substance into the body. Immediate interventions using various decontamination techniques targeting the gastrointestinal (GI) tract can achieve this. Decontamination is crucial to prevent poison from entering the systemic circulation, which involves washing affected areas with water and mild soap and removing contaminated clothing. Once external decontamination is done, attention must be turned to...
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Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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Zones of Protection01:16

Zones of Protection

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In power systems, the entire setup is divided into protective zones to isolate faults and protect the rest of the network. These zones include generators, transformers, buses, transmission lines, distribution lines, and motors. Each zone can be visualized as a separate room in a house, with each room protected by its own circuit breaker.
Protective zones are defined by closed dashed lines, containing one or more components. A key characteristic of these zones is the strategic placement of...
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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相关实验视频

Updated: May 6, 2026

A Pleural Effusion Model in Rats by Intratracheal Instillation of Polyacrylate/Nanosilica
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FortiNIDS:保护智能城市物联网基础设施免受基于机器学习的入侵检测系统中的可转移对手毒害.

Abdulaziz Alajaji1

  • 1Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
概括

本研究介绍了FortiNIDS,这是一个框架,用于增强基于AI的网络入侵检测系统 (NIDS) 来应对数据中毒攻击. 像对抗训练这样的防御措施可以提高智能城市物联网环境中的NIDS可靠性.

关键词:
物联网安全物联网安全物联网安全网络入侵检测系统 (NIDS) 是一种网络入侵检测系统.关于负面影响的拒绝 (RONI)具有对抗性的机器学习.进行对抗性培训.数据中毒攻击数据中毒攻击梯度增强可以提高梯度.随机的森林随机的森林智能城市安全 智能城市安全可以转让的可转让性.

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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:

  • 传统的安全机制不足以应对不断发展的网络攻击.
  • 基于AI的网络入侵检测系统 (NIDS) 提供先进的威胁检测,但容易受到数据中毒的影响.
  • 智慧城市物联网 (IoT) 环境面临着独特的安全挑战.

研究的目的:

  • 在树分类器 (随机森林,渐变增强) 上模拟黑子中毒攻击.
  • 引入 FortiNIDS,一个使用替代神经网络用于可转移的对抗性干扰的框架.
  • 评估防卫策略 (对抗训练,拒绝负面影响) 以提高智能城市中NIDS的弹性.

主要方法:

  • 在随机森林和渐变增强分类器上建模黑子中毒攻击.
  • 开发 FortiNIDS 框架,使用代用神经网络来生成对抗性示例.
  • 使用CICDDoS2019数据集评估对抗训练和拒绝负面影响 (RONI).

主要成果:

  • 证明了对抗性示例在模型之间的可转移性.
  • 福蒂NIDS框架有效地产生干扰来攻击NIDS.
  • 敌对训练和RONI显著提高了NIDS检测准确性和对攻击的可靠性.

结论:

  • 有针对性的防御策略增强了基于AI的NIDS对数据中毒的稳定性.
  • FortiNIDS提供了一种方法来理解和防御复杂的对抗性攻击.
  • 这些发现有助于确保智能城市物联网网络的安全,并保护用户隐私.