优化入侵检测系统的预测性能,使用安全系统的混合组合模型进行安全检测
Qaiser Abbas1, Sadaf Hina2, Hamza Sajjad3
1University of Engineering and Technology, Lahore, Pakistan.
PeerJ. Computer science
|September 14, 2023
概括
使用随机森林递归特征消除 (RF-RFE) 方法的新混合组合模型增强了网络入侵检测系统 (IDS). 这种具有成本效益的解决方案为传统和物联网网络提供了高精度,以更低的计算成本优于深度学习.
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:
- 网络入侵对组织安全构成重大威胁.
- 发展中国家的中小企业在实施有效的安全解决方案时面临资源限制.
- 对于传统网络和物联网 (IoT) 网络,需要强大的,具有成本效益的入侵检测系统 (IDS).
研究的目的:
- 提出一种新的混合组合模型,以提高入侵检测系统 (IDS) 的预测性能.
- 开发适合中小企业的具有成本效益和效率的IDS.
主要方法:
- 开发了一个混合组合模型,结合了随机森林和递归特征消除 (RF-RFE).
- 该模型在基准数据集上进行了评估:NSL-KDD,UNSW-NB15和CSE-CIC-IDS2018.
- 性能与深度学习范式进行了比较,重点关注准确性,计算成本和训练时间.
主要成果:
- 拟议的RF-RFE组合模型实现了高整体准确性:在NSL-KDD上达到99%,在UNSW-NB15上达到98.53%,在CSE-CIC-IDS2018.2018上达到99.9%.
- 机器学习组合方法展示了最先进的结果.
- 与深度学习方法相比,该模型以更低的计算成本和更短的培训时间实现了这些结果.
结论:
- 拟议的混合组合模型显著优化了入侵检测系统的性能.
- 这项研究有助于开发更安全的系统和应用程序,特别有利于资源有限的中小企业.
- 这些发现突显了机器学习组合方法在解决网络安全挑战方面的有效性.
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