Abstract: The network nodes are fragile, with many potential attack behaviors and redundant intersection situations, resulting in poor feature recognition accuracy and classification performance and low monitoring stability and efficiency. Therefore, a network potential attack monitoring based on fuzzy Markov game algorithm was studied. Using the fusion degree compressed sensing method and the feature recognition degree parameter analysis method, the random discrete distribution sequence of network potential attack characteristics is analyzed, the characteristics of network potential attack spectrum is also extracted and analyzed. The random forest algorithm is adopted to distinguish the types of potential network attacks, and the fuzzy Markov game analysis of potential network attack risk is carried out. According to the risk state set and the principle of minimum and maximum, the potential network attack risk is monitored. The test results of the example show that after the proposed method is applied, potential attack behavior parameters are set, and the fluctuation of potential attack recognition rate is small. The fuzzy Markov game analysis results are closest to the actual risk value, and have high recognition accuracy, monitoring efficiency, and monitoring stability. 

Key words: potential network attacks, feature extraction, random forest, risk fuzzy Markov game analysis