转移学习用于保护电动汽车充电基础设施免受网络物理攻击
Ahmad Almadhor1, Shtwai Alsubai2, Imen Bouazzi3,4
1Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia.
Scientific reports
|March 19, 2025
概括
本研究介绍了转移学习 (TL) 框架,以提高电动汽车充电站 (EVCS) 的安全性. 这种新的方法显著改善了对网络物理攻击的检测,为EVCS网络安全提供了更好的准确性和可扩展性.
科学领域:
- 网络安全 网络安全
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 电动汽车充电站 (EVCS) 的安全面临越来越多的网络威胁.
- 传统的入侵检测系统 (IDS) 缺乏检测新型或复杂攻击的能力.
- 现有的系统往往无法识别已知和未发现的威胁,因为它们依赖于传统的机器学习和功能选择.
研究的目的:
- 提出一个转移学习 (TL) 框架,以在EVCS中加强网络物理攻击的检测.
- 提高电动汽车充电基础设施中威胁检测的准确性和可扩展性.
- 解决传统IDS在识别复杂网络威胁方面的局限性.
主要方法:
- 开发了一个转移学习 (TL) 框架,使用预训练的深度神经网络 (DNN) 模型权重.
- 应用数据规范化和最小-最大缩放技术用于模型培训.
- 将TL模型的性能与长期短期记忆 (LSTM),循环神经网络 (RNN),LSTM-RNN和门式循环单元 (GRU) 模型进行了比较.
- 使用CICEVSE2024 (EVSE-A和EVSE-B) 数据集对框架进行了评估.
主要成果:
- 提议的TL模型在检测网络物理攻击方面实现了93%的准确性.
- 与其他评估的深度学习模型相比,TL框架表现优越.
- 该研究证实了预训练模型在提高EVCS安全性的有效性.
结论:
- 转移学习 (TL) 为改善EVCS网络安全提供了一个强大的解决方案.
- 该TL模型有效检测恶意攻击,提高电动汽车充电基础设施的整体安全性.
- 这种方法在EVCS安全性和恶意攻击检测之间提供了高度的对称性.
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