Machine learning algorithms against hacking attack and detection success comparison
2020 2nd International Conference on Smart Power & Internet Energy …, 2020•ieeexplore.ieee.org
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 …
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 …
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.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果