In order to accurately identify vulnerability risks,an automatic identification method for 0day vulnerability risks in power indus-try control networks is proposed.It converts the power industry control network into two-dimensional data according to require-ments,obtains the grayscale matrix through normalization processing,and uses two-dimensional wavelet threshold denoising method to denoise the data.Using conventional sequence feature extraction rules as the basic unit,feature extraction methods are used to extract the 0day vulnerability risk features of the power industry control network,and a feature set is constructed.All data in the set is mapped onto a two-dimensional plane,and data is distinguished through a Vinot map to achieve automatic identifica-tion of 0day vulnerability risk in the power industry control network.The experimental results show that the proposed method can effectively improve the accuracy of vulnerability risk automatic identification results,while also effectively reducing identifica-tion time.
power industrial control network0dayvulnerability riskautomatic recognition
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