基于meta-learner的方法来检测物联网网络上的攻击.
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)
|October 14, 2023
概括
本研究引入了一种超学习方法,用于识别物联网 (IoT) 网络攻击. 该方法通过结合深度学习模型来增强安全性,XGBoost实现了98.75%的准确性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 物联网 (IoT) 设备的扩散扩大了网络威胁的攻击面.
- 来自物联网设备的大量数据可以压倒传统的安全系统,阻碍有效的威胁检测.
- 现有的安全措施难以应对物联网网络漏洞的规模和复杂性.
研究的目的:
- 提出一种新的元学习框架,用于在物联网网络中增强攻击识别.
- 为应对越来越多的互联物联网设备和数据过载所带来的安全挑战.
- 评估一个meta-learner模型的有效性,该模型整合了多个深度学习和机器学习算法.
主要方法:
- 开发了一个超学习者,通过从重复神经网络 (RNN),长期短期记忆 (LSTM) 和卷积神经网络 (CNN) 模型中堆叠预测.
- 使用后勤回归 (LR),多层感知器 (MLP),支持向量机器 (SVM) 和极端梯度提升 (XGBoost) 进行元学习者识别.
- 使用2020年物联网数据集进行了广泛的评估,以评估模型性能.
主要成果:
- 极端梯度提升 (XGBoost) 模型实现了最高的准确性 (98.75%),精度 (98.30%),F1测量 (98.53%) 和AUC-ROC (98.75%).
- 支持矢量机 (SVM) 模型显示了最高的召回率 (98.90%),与XGBoost.com相比略有改善.
- 超学习方法有效地增强了物联网环境中的攻击检测能力.
结论:
- 拟议的元学习框架为识别复杂物联网网络中的攻击提供了强大的解决方案.
- 在物联网安全方面,XGBoost和SVM模型对实时威胁检测和缓解具有显著的前景.
- 这种方法提供了一个可扩展和有效的策略,以加强互联物联网生态系统的安全姿态.
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