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Machine Learning Algorithms Against Hacking Attack and Detection Success Comparison

机译:针对黑客攻击和检测成功比较的机器学习算法

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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.
机译:随着网络攻击风险的不断提高,电力系统保护部门已变得极为重要。为了创建可持续发展且受到良好保护的系统,电力系统数据必须健康。为此,已经开发了许多机器学习应用程序并将其用于错误数据检测。但是,每种方法都有不同的检测和应用过程。方法比其他方法有优势。尽管一种算法可以轻松检测到某些注入,但是当注入类型更改时,同一算法可能会失败。因此,当进样类型改变时,方法获得了不同的成功结果。因此,通过创建的特殊黑客算法将不同的注入类型应用于电力系统IEEE 14总线系统。 PSCAD和python链接已用于仿真和检测部件。创建了3种不同的进样类型并将其应用到系统上,并测试了5种不同的最流行算法(SVM,k-NN,LDA,NB,LR)。比较和评估每种算法的性能。

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