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基于LightGBM的大规模物联网攻击检测方案和使用改进的salp swarm算法的特征选择.

Weizhe Chen1, Hongyu Yang1, Lihua Yin2

  • 1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China.

Scientific reports
|August 19, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了GQBWSSA,这是一个用于物联网 (IoT) 网络威胁检测的增强型元启发算法. 它提高了特征选择的准确性和效率,优于对基准数据集的现有方法.

关键词:
组合学习学习 组合学习功能选择 功能选择物联网攻击检测检测物联网攻击检测这种算法是Metaheuristic算法.萨尔普队伍算法 萨尔普队伍算法

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科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 物联网 (IoT) 设备的快速增长增加了对网络攻击的脆弱性.
  • 现有的物联网攻击检测方法在准确性,高维度和模型效率方面扎.
  • 超启发式算法为网络数据中有效的特征选择提供了一个有希望的方法.

研究的目的:

  • 为物联网 (IoT) 开发一个高效准确的网络威胁检测方案.
  • 引入一个增强的元启发算法,GQBWSSA,以优化功能选择.
  • 使用LightGBM创建一个轻量级的集体学习模型,以改进物联网攻击检测.

主要方法:

  • 开发了一个增强的元启发算法,GQBWSSA (改进的Salp Swarm算法).
  • 使用GQBWSSA设计了一个基于门投票的特征选择框架,以减少数据维度.
  • 选择的功能与LightGBM算法集成,以创建用于攻击检测的集体学习模型.

主要成果:

  • 与现有的元启发算法相比,GQBWSSA在特征选择方面表现优越.
  • 拟议的集体学习方案实现了高准确度,精度,并缩短了训练/检测时间.
  • 在CICIoT2023数据集中,该方案在二进制分类方面达到99.70%的准确性,在多类分类方面达到99.41%的准确性.

结论:

  • GQBWSSA算法有效地解决了维度的诅咒,并改善了物联网安全的功能选择.
  • 拟议的基于LightGBM的集体学习模型为物联网网络威胁检测提供了一个轻量级和高效的解决方案.
  • 该研究验证了在NSLKDD和大规模CICIoT2023数据集上提出的方法的有效性.