在智能电网环境中对电动汽车充电站使用电网哨兵框架进行异常检测
V Thiruppathy Kesavan1,2, Md Jakir Hossen3, R Gopi2,4
1Faculty of Information Technology, Dhanalakshmi Srinivasan Engineering College, Perambalur, Tamil Nadu, India.
Scientific reports
|May 6, 2025
概括
一个新的框架,电网哨兵 (AD-GS),使用机器学习来保护电动汽车充电站在智能电网上. 它可以检测和应对威胁,提高电网安全,确保可靠的电动汽车基础设施.
科学领域:
- 网络安全 网络安全
- 智能电网技术 智能电网技术
- 可持续的运输可持续的运输
背景情况:
- 电动汽车 (EV) 的采用对于可持续运输至关重要,但智能电网上的充电站面临安全漏洞.
- 目前的系统缺乏持续监控,事件响应和强大的安全措施来应对复杂的攻击.
- 将智能电网系统与电动汽车充电基础设施集成,带来了独特的安全挑战.
研究的目的:
- 提出一个新的框架,机器学习授权异常检测与电网哨兵框架 (AD-GS),以保护电动汽车充电站.
- 加强支持电力运输的智能电网基础设施的安全性和可靠性.
- 在实时中动态检测和响应网络入侵和异常.
主要方法:
- 利用机器学习算法,包括长短期记忆 (LSTM),随机森林和自动编码模型用于异常检测.
- 开发了网络哨兵 (AD-GS) 框架,用于持续监控和动态威胁响应.
- 模拟了AD-GS架构,以测试其应对非凡攻击的弹性,并评估性能.
主要成果:
- 该AD-GS框架在检测异常 (96.8%) 和保护数据 (99.2%) 方面表现出高准确度.
- 通过快速威胁缓解,智能电网响应时间效率提高了98.4%.
- 在模拟攻击期间成功减少停机时间并保持充电站性能.
结论:
- AD-GS框架有效地提高了电动汽车充电站的智能电网安全和网络安全.
- 拟议的系统具有可扩展性,能够监控500多个站点并保护各种电网组件.
- AD-GS提供了一个强大的解决方案,适用于超越电动汽车充电,提高整体智能电网的弹性.
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