在电动汽车充电系统中检测网络攻击,使用剩余的使用寿命产生对抗网络
Hayriye Tanyıldız1, Canan Batur Şahin1, Özlem Batur Dinler1
1Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Malatya, 44210, Turkey.
Scientific reports
|March 25, 2025
概括
本研究引入了使用生成对抗网络 (GAN) 预测电动汽车充电站网络攻击的剩余使用寿命 (RUL) 方法. GAN-GRU模型表现出高精度,增强了电动汽车的网络安全策略.
科学领域:
- 网络安全 网络安全
- 电动汽车基础设施电动汽车基础设施
- 机器学习 机器学习
背景情况:
- 电动汽车 (EV) 和互联充电基础设施 (EVSE) 的日益普及加剧了网络安全风险.
- 对EVSE的网络攻击对经济和声誉构成重大威胁.
- 积极的网络安全策略对于电动汽车生态系统的弹性至关重要.
研究的目的:
- 建议使用剩余使用寿命 (RUL) 方法来估计EVSE.网络攻击的持续时间.
- 评估RUL估计对加强EVSE网络安全战略的影响.
- 利用生成对抗网络 (GAN) 进行先进的网络攻击预测.
主要方法:
- 在置和充电状态下对EVSE的网络和主机攻击的评估RUL估计.
- 与深度学习模型进行比较,包括GRU,LSTM,RNN,CNN,MLP和与GAN集成的密集层.
- 使用MAE,RMSE,MSE和R平方指标评估模型性能.
主要成果:
- 该GAN-GRU模型实现了最高的准确性与最低的MAE (0.0281).
- GAN-CNN模型在错误一致性和差异性方面表现出优异的整体性能.
- 整合GAN提高了跨模型的预测准确度和早期攻击检测能力.
结论:
- 使用GAN的RUL方法提供了一种积极的方法来加强EVSE网络安全.
- 准确的RUL预测使得能够做出明智的维护和操作决策.
- 用GAN增强的深度学习模型显著降低了错误率,并改善了对电动汽车充电基础设施的攻击预测.
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