在基于SDN的SCADA系统中进行DDoS检测的Ensemble学习框架
Saadin Oyucu1, Onur Polat2, Muammer Türkoğlu3
1Department of Computer Engineering, Adıyaman University, Adıyaman 02040, Turkey.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
本研究介绍了一种基于决策树的集体学习方法,用于检测基于软件定义网络 (SDN) 的监督控制和数据采集 (SCADA) 系统中的分布式拒绝服务 (DDoS) 攻击. 拟议的技术增强了可再生能源管理的网络安全.
科学领域:
- 能源系统中的网络安全.
- 机器学习用于网络安全.
- 可再生能源基础设施管理管理可再生能源基础设施管理
背景情况:
- 监督控制和数据采集 (SCADA) 系统对于可再生能源至关重要,但传统的基础设施面临着扩展和管理的挑战.
- 将软件定义网络 (SDN) 与SCADA集成提供了好处,但增加了网络安全风险,特别是来自分布式拒绝服务 (DDoS) 攻击.
- 网络物理能源系统 (CPES) 需要强有力的安全措施来应对破坏能源资源和增加运营成本的威胁.
研究的目的:
- 为基于SDN的SCADA系统提供有效的入侵检测系统,以防止DDoS攻击.
- 开发基于决策树的集体学习技术,以准确检测DDoS流量.
- 提高可再生能源管理系统的安全性和可靠性.
主要方法:
- 基于决策树的集体学习技术被用来区分正常和DDoS攻击流量.
- 使用特征选择和超参数调整来优化组合模型的性能.
- 通过模拟实验网络拓,收集和分析了正常和DDoS攻击流量数据.
主要成果:
- 拟议的集体学习模型证明了在基于SDN的SCADA系统中准确检测DDoS攻击.
- 功能选择,模型组合和超参数调整显著提高了检测模型的准确性和性能.
- 该研究证实了机器学习方法在识别恶意流量的有效性.
结论:
- 基于决策树的集体学习技术为在基于SDN的SCADA系统中检测DDoS攻击提供了有效的解决方案.
- 优化机器学习模型对于提高网络物理能源系统的网络安全至关重要.
- 这项研究有助于保护可再生能源基础设施免受复杂的网络威胁.
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