Power Mobile Terminal Security Assessment Based on Weights Self-Learning

Abstract

At present, mobile terminals are widely used in power system and easy to be the target or springboard to attack the power system. It is necessary to have security assessment of power mobile terminal system to enable early warning of potential risks. In the context, this paper builds the security assessment system against to power mobile terminals, with features from security assessment system of general mobile terminals and power application scenarios. Compared with the existing methods, this paper introduces machine learning to the Rank Correlation Analysis method, which relies on expert experience, and uses objective experimental data to optimize the weight parameters of the indicators. From experiments, this paper proves that weights self-learning method can be used to evaluate the security of power mobile terminal system and improve credibility of the result.