Visible to the public Power Mobile Terminal Security Assessment Based on Weights Self-Learning

TitlePower Mobile Terminal Security Assessment Based on Weights Self-Learning
Publication TypeConference Paper
Year of Publication2018
AuthorsXi, Z., Chen, L., Chen, M., Dai, Z., Li, Y.
Conference Name2018 10th International Conference on Communication Software and Networks (ICCSN)
Date Publishedjul
KeywordsCorrelation, expert systems, fuzzy comprehensive analysis, Hardware, Human Behavior, Indexes, learning (artificial intelligence), machine learning, mobile computing, power engineering computing, power mobile terminal, power mobile terminal security assessment, power mobile terminal system, power system security, pubcrawl, rank correlation analysis, resilience, Resiliency, Scalability, security, security assessment, security assessment system, security of data, Software, Training, weights self-learning method

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.

Citation Keyxi_power_2018