期刊论文

Niandong, Liao

英文

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

1064-1246

2020

39

1

433-447

10.3233/JIFS-191448

The authors are grateful to the anonymous reviewers for their detailed and accurate comments on the amendments to this paper. This work is supported in part by the Natural Science Foundation of China (Nos. U196620027, 51777015), and also supported by the project of “Practical Innovation and Enhancement of Entrepreneurial Ability (No. SJCX201970)” for Professional Degree Postgraduates of Changsha University of Science & Technology; Open fund project of Hunan Provincial Key Laboratory of Processing of Big Data on Transportation (No. A1605); the key scientific and technological project of “Research and Application of Key Technologies for Network Security Situational Awareness of Electric Power Monitoring System (No. ZDKJXM20170002)” of China Southern Power Grid Corporation. The authors are grateful to the anonymous reviewers for their detailed and accurate comments on the amendments to this paper. This work is supported in part by the Natural Science Foundation of China (Nos. U196620027, 51777015), and also supported by the project of ""Practical Innovation and Enhancement of Entrepreneurial Ability (No. SJCX201970)"" for Professional Degree Postgraduates of Changsha University of Science & Technology; Open fund project of Hunan Provincial Key Laboratory of Processing of Big Data on Transportation (No. A1605); the key scientific and technological project of ""Research and Application of Key Technologies for Network Security Situational Awareness of Electric Power Monitoring System (No. ZDKJXM20170002)"" of China Southern Power Grid Corporation.

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Aiming at the problems of excessive dependence on manual work, low detection accuracy and poor real-time performance of current probe flow anomaly detection in power system network security detection, a detection method for calculating information entropy of probe flow and random forest classification is proposed. Firstly, the network probe stream data are captured and aggregated in real-time to extract network stream metadata. Secondly, by calculating Pearson correlation coefficient and maximum mutual information coefficient, feature selection...