用人工智能驱动的网络安全框架用于电力系统中异常检测.
Vignes V M1, Sri Harini M P1, Rahul Satheesh2
1Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India.
Scientific reports
|October 10, 2025
概括
本研究介绍了智能电网的AI网络安全框架,通过融合网络和物理数据来增强异常检测. 它实现了对网络威胁的高精度和弹性,确保了可靠的电力系统运行.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 电力系统工程 电力系统工程
背景情况:
- 智能电网面临着越来越多的网络威胁,如虚假数据注入攻击 (FDIA),拒绝服务 (DoS) 和中间人 (MiTM) 攻击.
- 由于缺乏上下文意识和实时适应能力,传统的安全方法是不够的.
- 物联网和自动化在智能电网中的整合加剧了这些威胁的复杂性和频率.
研究的目的:
- 引入人工智能驱动的网络安全框架,用于在电力系统中高精度的异常检测.
- 融合网络和物理数据集,以加强威胁识别.
- 提高智能电网安全的弹性和可解释性.
主要方法:
- 利用长短期记忆 (LSTM) 网络和随机森林分类器进行异常检测.
- 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).
- 实施基于FGSM的对抗训练,以提高对抗攻击的稳定性.
主要成果:
- 在二进制分类中获得了99.798%的准确性,在多类分类中获得了98.1919%的准确性.
- 通过对抗训练,对抗准确度从95.15%提高到99.39%.
- 在Xilinx PYNQ-Z2边缘设备上在2.16秒内完成模型推理的实用可行性.
结论:
- 拟议的AI框架为智能电网网络安全提供了高效率,可解释性和运营弹性.
- 网络和物理数据的融合显著提高了异常检测能力.
- 该框架非常适合在智能电网环境中实时部署,以确保安全可靠的电力系统运行.
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