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相关概念视频

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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...
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在软件定义网络中基于特征工程和机器学习的DDoS检测方法.

Zhenpeng Liu1,2, Yihang Wang1, Fan Feng2

  • 1School of Electronic Information Engineering, Hebei University, Baoding 071002, China.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种机器学习方法,用于检测软件定义网络 (SDN) 中的分布式拒绝服务 (DDoS) 攻击. 随机森林在识别这些重大网络安全威胁方面取得了最佳表现.

关键词:
对于DDoS攻击来说,这是一次性攻击.二进制灰狼优化算法二进制灰狼优化算法功能工程的特点工程.机器学习是机器学习.软件定义网络是软件定义的网络.

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科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 机器学习应用 机器学习应用

背景情况:

  • 分布式拒绝服务 (DDoS) 攻击对软件定义网络 (SDN) 的稳定性和安全性构成重大威胁.
  • 有效的检测机制对于保持SDN基础设施的完整性和可用性至关重要.
  • 现有的检测方法可能需要优化,以提高准确性和效率.

研究的目的:

  • 提出和评估一种基于功能工程和机器学习的新方法,用于检测SDN中的DDoS攻击.
  • 确定SDN环境中用于DDoS攻击检测的最有效的机器学习算法.
  • 提供强大的解决方案,增强SDN安全性,防止复杂的网络威胁.

主要方法:

  • 利用了CSE-CIC-IDS2018数据集,涉及数据清理,规范化和功能选择,使用改进的二进制灰狼优化算法.
  • 训练并评估了多个机器学习分类器,包括随机森林 (RF),支持矢量机 (SVM),K-最近邻居 (k-NN),决策树和XGBoost.
  • 根据性能指标选择了最佳分类器,并将其部署在SDN控制器中进行实时检测.

主要成果:

  • 随机森林 (RF) 算法在各种指标上表现出卓越的性能,例如准确性,精度,回忆,F1得分和AUC值.
  • 对比分析证实了拟议的特征工程和基于射频的检测方法对其他评估算法的有效性.
  • 开发的模型成功地检测和识别了SDN环境中的DDoS攻击.

结论:

  • 拟议的机器学习方法,特别是随机森林分类器,为检测SDN中的DDoS攻击提供了有效的解决方案.
  • 功能工程与优化机器学习相结合,显著提高了在软件定义网络中识别和减轻网络威胁的能力.
  • 这项研究为SDN安全领域做出了有价值的贡献,提供了一个实用且高性能的检测策略.