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用子搜索算法对贝叶斯机器学习进行建模,用于在IIoT环境中检测网络攻击
Faten Khalid Karim1, José Varela-Aldás2, Mohamad Khairi Ishak3
1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Scientific reports
|November 26, 2024
概括
本研究介绍了一种新的贝叶斯式机器学习与Sparrow搜索算法用于网络攻击检测 (BMLSSA-CAD) 技术,以提高工业物联网 (IIoT) 的安全性. BMLSSA-CAD方法有效地检测网络攻击,在基准数据集上实现高精度.
科学领域:
- 网络安全 网络安全
- 事物的工业互联网 (IIoT)
- 机器学习 机器学习
背景情况:
- 工业物联网 (IIoT) 正在迅速扩展,连接众多设备并实现自动化.
- 不断连接使IIoT系统易受复杂的网络攻击.
- 现有的入侵检测系统 (IDS) 需要增强的方法来有效地检测IIoT中的网络攻击.
研究的目的:
- 提出一种新的技术,贝叶斯式机器学习与Sparrow搜索算法用于网络攻击检测 (BMLSSA-CAD),用于IIoT网络的强大安全.
- 提高IIoT环境中检测网络攻击的准确性和效率.
- 为了利用先进的机器学习和优化算法来提高IIoT安全性.
主要方法:
- 使用min-max缩放器规范化的数据预处理.
- 通过Chameleon优化算法 (COA) 进行功能选择.
- 使用贝叶斯信念网络 (BBN) 模型检测网络攻击,并通过Sparrow Search Algorithm (SSA) 进行超参数调整.
主要成果:
- BMLSSA-CAD技术在检测网络攻击方面表现出了很高的性能.
- 在UNSWNB51和UCI SECOM数据集上分别获得了97.84%和98.93%的卓越准确率.
- 在IIoT网络攻击检测的实验验证中表现优于最近的技术.
结论:
- 拟议的BMLSSA-CAD技术为IIoT安全提供了显著的进步.
- BBN,COA和SSA的整合为检测网络威胁提供了一个有效的框架.
- 这种方法提高了工业系统对网络攻击的可靠性和安全性.
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