双重卷积神经网络方法在物联网网络上进行特征选择和攻击检测.
Basim Ahmad Alabsi1, Mohammed Anbar2, Shaza Dawood Ahmed Rihan1
1Applied College, Najran University, Kind Abdulaziz Street, Najran P.O. Box 1988, Saudi Arabia.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种新的CNN-CNN方法来检测物联网 (IoT) 攻击. 该方法有效地识别高精度的网络威胁,增强物联网安全性.
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) 设备的普及扩大了全球连接.
- 越来越多的物联网设备使用增加了网络对各种网络威胁的脆弱性.
研究的目的:
- 为物联网网络开发和评估一个强大的攻击检测系统.
- 通过先进的威胁识别来增强物联网生态系统的安全性和完整性.
主要方法:
- 提出了一个混合深度学习模型,将两个卷积神经网络 (CNN-CNN) 结合起来.
- 第一个CNN模型从网络流量数据中提取了重要的特征.
- 第二个CNN模型利用这些特性构建了一个准确的攻击检测系统.
主要成果:
- 在BoT IoT 2020数据集上,CNN-CNN方法实现了98.04%的检测准确度.
- 记录了高精度 (98.09%) 和回忆 (99.85%),虚假阳性率低 (1.93%).
- 与现有的深度学习算法和特征选择技术相比,拟议的方法显示出更高的性能.
结论:
- 开发的CNN-CNN模型为在物联网环境中检测攻击提供了高度有效的解决方案.
- 这种方法显著改进了当前识别和减轻物联网网络威胁的方法.
- 这些发现强调了深度学习的潜力,以确保连接设备的不断扩展的景观.
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