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相关概念视频

Multimachine Stability01:25

Multimachine Stability

150
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
150
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

118
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:
118
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

241
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
241

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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基于和机器学习的方法用于在软件定义网络中检测DDoS攻击.

Amany I Hassan1, Eman Abd El Reheem2, Shawkat K Guirguis2

  • 1Institute of Graduate Studies and Research, Alexandria, Egypt. igsr.amany_ibrahim@alexu.edu.eg.

Scientific reports
|August 5, 2024
PubMed
概括

本研究介绍了一种结合统计分析和机器学习的混合方法,用于检测和减轻软件定义网络 (SDN) 中的分布式拒绝服务 (DDoS) 攻击. 该方法有效地识别和阻止快速攻击,增强SDN安全性.

科学领域:

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

背景情况:

  • 软件定义网络 (SDN) 提供高效的网络管理,但面临着严重的分布式拒绝服务 (DDoS) 威胁.
  • 现有的DDoS检测方法在SDN环境中与不断变化的攻击向量作斗争.

研究的目的:

  • 提出和评估一种新的混合方法,用于在SDN环境中检测和减轻DDoS攻击.
  • 加强SDN基础设施的安全性和可用性,以应对复杂的网络威胁.

主要方法:

  • 一个混合检测系统,将基于的统计分析与机器学习 (k-means集群) 结合起来.
  • 该系统分析用户对系统的影响,以确定异常活动.
  • 使用CIC-IDS2017,CSE-CIC-2018和CICIDS2019数据集进行实验验证.

主要成果:

  • 拟议的混合方法在检测和阻止突然和快速的DDoS攻击方面表现出了很高的有效性.
  • 统计和机器学习方法的结合提高了攻击识别准确度.
  • 观察到SDN安全性对抗DDoS威胁的显著增强.

结论:

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  • 这种新的混合方法为SDN中DDoS攻击的检测和缓解提供了一个强大的解决方案.
  • 这种方法有可能大大提高SDN环境对网络攻击的弹性.
  • 进一步的研究可以探索先进的机器学习算法,以提高性能.