通过深度组合模型,通过先进的数学建模来缓解智能电网中的安全威胁
Sanaa A Sharaf1, Mahmoud Ragab2, Nasser Albogami3
1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Scientific reports
|October 4, 2024
概括
本研究介绍了一种用于智能电网 (SG) 的新型入侵检测系统 (IDS),增强网络安全. 山优化与深度集体学习 (MGODEL-ID) 模型显著改善了威胁检测和电网可靠性.
科学领域:
- 网络安全 网络安全
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 智能电网 (SG) 容易受到网络攻击,需要采取强有力的安全措施.
- 侵入检测系统 (IDS) 对于保护SG数据和确保可靠的电源供应至关重要.
- 深度学习 (DL) 提供了高级功能,用于识别SG中的复杂攻击模式.
研究的目的:
- 为智能电网开发一种新的入侵检测技术.
- 提高智能电网环境对抗网络威胁的弹性和安全性.
- 为了提高入侵检测,利用深度学习和元启发式优化.
主要方法:
- 开发了一种新的基于MGODEL-ID的入侵检测技术.
- 数据预处理涉及Z分数规范化,并使用山地鱼优化 (MGO) 模型进行特征选择.
- 侵入检测是使用长期短期记忆 (LSTM),深度自编码器 (DAE) 和极端学习机器 (ELM) 分类器进行的,通过虫优化器 (DBO) 进行超参数调整.
主要成果:
- 在智能电网环境中检测入侵时,MGODEL-ID模型表现出卓越的性能.
- 模拟结果证实了拟议的MGODEL-ID技术所取得的增强安全结果.
- 实验验证表明,MGODEL-ID的性能优于现有的入侵检测模型.
结论:
- MGODEL-ID技术为智能电网网络安全提供了强大而有效的解决方案.
- 深度学习和元启发优化的整合显著提高了入侵检测能力.
- 开发的模型有助于电力供应链的完整性,弹性和可靠性.
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