自动传感器节点恶意活动检测与可解释性分析
Md Zubair1, Helge Janicke2, Ahmad Mohsin2
1Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
本研究介绍了一种混合数据平衡技术和一个集成机器学习模型,以有效地检测网络安全系统中的恶意传感器节点,实现高精度.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 现代自动化系统在决策方面严重依赖传感器.
- 针对传感器的恶意活动可能会导致整个系统的故障.
- 检测恶意传感器活动对于系统的安全和保障至关重要.
研究的目的:
- 开发一种强大的方法来检测恶意传感器节点.
- 为了应对不平衡的数据集在检测恶意活动的挑战.
- 提高机器学习模型在网络安全中的可解释性.
主要方法:
- 提出了一种混合数据平衡技术,结合了基于集群的低采样和合成少数群体过量采样技术 (SMOTE).
- 开发了一种集体机器学习模型,以提高检测准确度.
- 进行了可解释性分析,以确定关键的安全风险特征.
主要成果:
- 拟议的混合数据平衡技术有效地处理不平衡的数据集.
- 整体机器学习模型在检测恶意传感器节点方面实现了99.7%的准确性.
- 确定了导致传感器节点安全风险的关键特征.
结论:
- 混合数据平衡和组合模型为检测恶意传感器节点提供了可靠的解决方案.
- 这种方法显著提高了基于传感器的自动化系统的网络安全.
- 可解释性分析为传感器节点漏洞提供了有价值的见解.
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