基于组合特征选择和深度学习模型的物联网网络攻击检测方法.
Shaza Dawood Ahmed Rihan1, Mohammed Anbar2, Basim Ahmad Alabsi1
1Applied College, Najran University, King Abdulaziz Street, Najran P.O. Box 1988, Saudi Arabia.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究引入了一种新的方法来检测物联网 (IoT) 攻击,通过将整体特征选择与深度学习模型相结合. 该方法显著提高了物联网安全系统的准确性和可靠性.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 物联网 (IoT) 设备的普及导致了网络安全漏洞的增加.
- 物联网攻击对组织和个人构成重大威胁,需要先进的检测方法.
研究的目的:
- 提出和评估一种有效的方法来检测物联网网络中的攻击.
- 通过整体特征选择增强的深度学习模型的性能评估.
主要方法:
- 使用集成特征选择,结合过方法 (差异值,相互信息,Chi-square,ANOVA,L1) 与递归特征消除 (RFE).
- 评估了选定的功能对各种深度学习 (DL) 模型的影响,包括CNN,RNN,GRU和LSTM.
- 在IoT-Botnet 2020数据集上测试了拟议的方法,测量了检测准确度,精度,回忆,F1测量和假阳性率 (FPR).
主要成果:
- 所有评估的DL模型都表现出高性能,检测准确度在97.05%至97.87%之间.
- 精度,回忆和F1测量值也特别高,分别在96.99%至97.95%,99.80%至99.95%,98.45%至98.87%之间.
- 从组合选择中获得的精细功能集显著改善了DL模型在物联网攻击检测中的性能.
结论:
- 拟议的整体特征选择与DL模型相结合,为检测物联网网络攻击提供了强大的解决方案.
- 这些发现强调了这种混合方法在改善物联网环境的网络安全方面的有效性.
- 这项研究通过先进的威胁检测,有助于开发更安全可靠的物联网生态系统.
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