在SDWSN中检测LDoS攻击的方法基于压缩的希尔伯特-黄变换和卷积神经网络
Yazhi Liu1,2, Ding Sun1,2, Rundong Zhang3
1College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了一种有效的方法,用于检测软件定义无线传感器网络 (SDWSNs) 中的低速拒绝服务 (LDoS) 攻击. 该技术通过使用压缩的希尔伯特-黄变换和卷积神经网络分析网络数据,达到99.8%的准确性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 信号处理 信号处理
背景情况:
- 低速拒绝服务 (LDoS) 攻击对软件定义无线传感器网络 (SDWSNs) 构成重大威胁.
- 这些攻击由于信号强度低且资源密集型,难以检测.
- 现有的检测方法与LDoS攻击信号的微妙特征作斗争.
研究的目的:
- 提出一个有效和准确的检测方法,用于SDWSNs中的LDoS攻击.
- 为了应对检测LDoS攻击特征的小,不平滑的信号的挑战.
- 提高计算效率,减少信号分析中的模式混合.
主要方法:
- 使用希尔伯特-黄变换 (HHT) 进行网络交通信号的时间频率分析.
- 实施压缩的HHT以删除冗余的内在模式函数 (IMF),提高效率并消除模式混合.
- 将一维数据转换为二维的时间光谱特征.
- 使用卷积神经网络 (CNN) 来对LDoS攻击进行分类和检测.
- 在网络模拟器-3 (NS-3) 环境中模拟LDoS攻击以进行性能评估.
主要成果:
- 提出的方法成功地将一维数据转化为二维时间光谱特征.
- 压缩的HHT有效降低了计算负载,并减轻了模式混合.
- 模拟演示了该方法在检测各种LDoS攻击场景方面的能力.
- 复杂多样化的LDoS攻击的检测准确率达到令人印象深刻的99.8%.
结论:
- 开发的方法提供了一个高度准确和高效的解决方案,用于检测SDWSNs中的LDoS攻击.
- 压缩HHT和CNN的集成提供了一个强大的框架来分析微妙的网络攻击信号.
- 这种方法显著提高了SDWSNs对复杂的拒绝服务威胁的安全姿态.
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