机器学习算法抗黑客攻击与检测成功比较

Levent Yavuz, Ahmet Soran, A. Önen, S. Muyeen
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引用次数: 0

摘要

随着网络攻击风险的日益增加,电力系统保护装置显得尤为重要。为了创建可持续的、保护良好的系统,电力系统数据必须是健康的。为此,已经开发了许多机器学习应用程序,并将其用于不良数据检测。但每种方法的检测和应用过程各不相同。方法比其他方法有优越性。虽然一种算法可以很容易地检测到某些注入,但当注入类型发生变化时,同样的算法可能会失效。因此,在不同注射类型的情况下,不同方法的成功率不同。为此,通过创建特殊的黑客算法,对电力系统IEEE 14总线系统进行了不同类型的注入。采用PSCAD和python联动进行仿真和检测部分。在系统上创建并应用了3种不同的注入类型,并测试了5种最流行的算法(SVM, k- NN, LDA, NB, LR)。对各算法的性能进行了比较和评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Algorithms Against Hacking Attack and Detection Success Comparison
Power system protection units has got enormous importance with the growing risk of cyber-attacks. To create sustainable and well protected system, power system data must be healthy. For that purpose, many machine learning applications have been developed and used for bad data detection. However, each method has got different detection and application process. Methods has superiority over other methods. Although, an algorithm can detect some injections easily, same algorithm can be fail when injection type changed. So methods have got different success results when the injection types changed. For that reason, different injection types are applied on power system IEEE 14 bus system via created special hacking algorithm. PSCAD and python linkage has been used for simulation and detection parts. 3 different injection types created and applied on the system and five different most popular algorithms (SVM, k- NN, LDA, NB, LR) tested. Each algorithm’s performances are compared and evaluated.
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