Open AccessAn Adaptive abnormal flow detection method for new energy stations based on HHT algorithm
Author(s) -
Yin Liang,
Xiaoqian Chen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1827/1/012206
Subject(s) - computer science , energy (signal processing) , anomaly detection , alarm , real time computing , constant false alarm rate , identification (biology) , false alarm , intrusion detection system , data mining , algorithm , artificial intelligence , engineering , statistics , botany , mathematics , biology , aerospace engineering
With the development of new energy technology, new energy stations are becoming more intelligent and data-based, and cyber-attacks on new energy stations are increasing year by year. In response to the continuous threats brought by malicious traffic to the network of new energy stations, this paper researches on the traffic anomaly detection technology based on network communication characteristics. An adaptive abnormal traffic detection method for new energy stations based on HHT algorithm is proposed, which improves the efficiency of identifying abnormal network traffic and more accurately identifies network attacks against new energy stations. It is verified through experiments that compared with mainstream classifiers, the method studied in this paper can achieve adaptive detection while adaptively determining the threshold, and the detection accuracy can reach 95%, the false alarm rate is lower than other methods, it can provide more accurate identification results for new energy field station network cyber-attacks detection.