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针对物联网网络的入侵检测数据的多标签分类的HOMLC-超参数优化
Ankita Sharma1, Shalli Rani1, Dipak Kumar Sah2
1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
这项研究比较了入侵检测的低级学习模型,发现超参数调整显著提高了性能. 低级别的CNN-MLP显示了多标签攻击分类的有希望的结果.
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:
- 侵入检测系统 (IDS) 对网络安全至关重要.
- 网络攻击的多标签分类是一个重大挑战.
- 基于低级别的学习模型为复杂的数据分析提供了一个有希望的方法.
研究的目的:
- 为了比较基于低级别的机器学习和深度学习模型在入侵检测中的多标签攻击分类的性能.
- 调查超参数优化对模型性能的影响.
- 评估混合数据集的模型,将公共入侵检测数据结合起来.
主要方法:
- 研究的基于低等级表示 (LRR) 和非负低等级表示 (NLR) 的模型:LR-SVM,LR-CNN和LR-CNN-MLP.
- 使用高斯贝叶斯优化来进行超参数调整.
- 在混合数据集上评估的模型合并了BoT-IoT和UNSW-NB15.
- 使用精度,回忆,F1得分和准确度评估性能.
主要成果:
- 所有三个低级型号在超参数调整后都显示出更好的性能.
- 低级别的CNN-MLP在多标签攻击分类中取得了显著的结果.
- 在分析,DoS和shellcode之间,UDP标签被准确地分类,准确度为98.54%.
结论:
- 超参数调整对于提高低级模型在入侵检测中的有效性至关重要.
- 基于低级别的深度学习模型,特别是LR-CNN-MLP,对于多标签攻击分类是有效的.
- 该研究强调了混合数据集和优化模型对于强大的网络安全的重要性.
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