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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

134
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
134
Classification of Systems-I01:26

Classification of Systems-I

222
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
222
Types of Reports II: Incident or Occurrence Report01:21

Types of Reports II: Incident or Occurrence Report

893
An Incident or Occurrence Report in a healthcare setting is a crucial document used to record any unexpected occurrence that may or may not have affected a patient, employee, or visitor. Such reports are critical to improving patient safety and include all details leading up to and including the event.
Purposes:
In the healthcare industry, reports play a crucial role in documenting incidents within an agency. The primary objective of these reports is to ensure patient safety, uphold the...
893
Classification of Systems-II01:31

Classification of Systems-II

183
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
183
Classification of Signals01:30

Classification of Signals

556
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
556
Fault Types01:18

Fault Types

109
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
109

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相关实验视频

Updated: Jul 27, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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在NFV中基于机器学习的异常检测:一项全面的调查

Sehar Zehra1,2, Ummay Faseeha1,3, Hassan Jamil Syed1,4,5

  • 1FAST School of Computing, National University of Computer and Emerging Sciences, Karachi 75030, Pakistan.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
概括
此摘要是机器生成的。

物联网和传感器网络中的网络功能虚拟化 (NFV) 安全性通过异常检测得到增强. 这项调查确定了机器学习算法,以有效地减轻网络攻击并保护网络完整性.

关键词:
物联网 (IoT) 的物联网 (IoT) 的物联网.检测异常检测异常检测网络攻击,网络攻击.基于机器学习的机器学习.网络功能的虚拟化 (NFV)面临的安全挑战监督学习学习监督学习没有监督的学习学习.

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相关实验视频

Last Updated: Jul 27, 2025

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 网络工程 网络工程

背景情况:

  • 网络功能虚拟化 (NFV) 提供了显著的好处,如降低成本和灵活性.
  • 对于优化传感器和物联网 (IoT) 网络而言,NFV至关重要.
  • 采用NFV引入了复杂的安全挑战.

研究的目的:

  • 探索NFV的安全挑战,特别是在物联网和传感器网络中.
  • 提出异常检测技术,以减轻NFV环境中的网络威胁.
  • 评估机器学习算法,以有效检测NFV网络中的异常.

主要方法:

  • 对NFV安全现有文献的调查.
  • 对异常检测技术的分析,重点是机器学习.
  • 评估各种ML算法用于NFV的基于网络的异常检测.

主要成果:

  • 在NFV部署中确定了关键的安全漏洞.
  • 评估了不同机器学习算法的异常检测效率.
  • 突出了评估算法的优缺点.

结论:

  • 异常检测,特别是使用机器学习,对于确保NFV至关重要.
  • 为及时缓解威胁提供了对最有效算法的洞察.
  • 旨在加强强大的传感器和物联网系统的NFV安全性.