智能电网安全:一种有效的基于CNN的混合方法,用于使用消费模式检测能源盗窃
Muhammed Zekeriya Gunduz1, Resul Das2
1Department of Computer Science and Technology, Vocational School of Technical Sciences, Bingöl University, Bingöl 12000, Türkiye.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
这项研究提出了一种混合深度学习系统,用于检测智能电网中的能源盗窃. 该系统准确地识别恶意用户操纵智能电表数据,增强电网安全,减少财务损失.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络安全 网络安全
背景情况:
- 智能电网依赖智能电表来收集数据,这对于负载监控和能源管理至关重要.
- 通过恶意消费者改数据来窃取能源是一个重大的全球挑战,造成了巨大的财务和技术损失.
- 现有的方法很难有效地检测对智能电表读数的复杂数据操纵攻击.
研究的目的:
- 开发和评估一种混合深度学习系统,用于检测智能电网中的能源盗窃.
- 为了应对数据改的挑战,针对智能电表消费数据的网络攻击.
- 提高能源盗窃检测系统的准确性和稳定性.
主要方法:
- 开发了一个混合系统,将卷积神经网络 (CNN) 结合起来,用于特征提取和传统的机器学习算法进行分类.
- 模拟了六种不同的数据改攻击向量,以生成现实的改数据集.
- 使用生成对抗网络 (GAN) 方法来解决数据不平衡,并通过合成数据增强模型训练.
- 设计和评估了针对个别攻击载体的专用探测器和针对所有攻击的通用探测器.
主要成果:
- 拟议的基于CNN的混合系统在分类诚实和恶意用户方面表现出令人满意的准确性.
- 在一个包含所有攻击载体的数据集上训练的通用探测器,在识别能源盗窃方面证明是有效的.
- 应用GAN用于数据增强显著改善了检测系统的培训过程.
结论:
- 开发的基于CNN的混合系统为检测智能电网中的能源盗窃提供了有希望的解决方案.
- 拟议的方法有效地识别了数据改网络攻击,减轻了非技术性损失.
- 这项研究强调了深度学习和GAN在确保智能电网基础设施方面的潜力.
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