Masquerade Detection Using Support Vector Machines in the Smart Grid
Xiang Zhao, Guangyu Hu, Zhigong Wu · 2014
In the Smart grid, network security is the important part. In this paper, we will introduce a new method detection based on Support Vector Machines to detect Masquerade attack, and test it and other methods on the dataset from keyboard commands on a UNIX platform. The presence of shared tuples would cause many attacks in this dataset to be difficultly detected, just as other researchers shown. In order to eliminate their negative influence on masquerade detection, we take some preprocessing for the dataset before detecting masquerade attacks. Our results show that after removing the shared tuples, the classifiers based on support vector machines outperforms the original approaches presented.