A novel communication network security situational awareness method based on improved Markov algorithm was studied to address the issues of long unit computation time and large errors in power line carrier communication network security situational awareness.The partition collection and dimensionality reduction operation data preprocessing were adopted to remove interference factors from power line carrier signals.Utilizing the membership association matrix to mine the features of network security elements,a hierarchical Markov network security situational awareness model was constructed.The BW algorithm was utilized to find the optimal solution of target parameters to determine the position of perception target points,shorten mining time,and improve perception accuracy.After experimental verification,the proposed method has a unit perception time of only 60～90 ms,and the mean square error of multiple sets of parallel perception does not exceed 2％,indicating that the proposed method can meet the application requirements of power line carrier communication network security situational awareness.
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