Long-Periodicity Detection from Massive Alarms in Power System
Yunfan Yang, Xinyu Yang, Hao Huang, Shilong Zhang, Qinqin Wu, Yanan Li, Zhenwei Gu, Yang Liu · 2024
The advent of big data and cloud computing has ushered in a new era of complexity in cybersecurity. Analysis of network security situations has emerged as a pivotal aspect of safeguarding networks. However, with the escalating volume of alarms and the enhancement of attack techniques, the task of network security analysis has grown increasingly daunting. The power system faces numerous challenges in defending its information security, including analyzing vast amounts of data, mining alarm correlations, and detecting high-risk alarms. In this paper, we propose a long-periodicity detection method for alarms over a long time span, aiming to reduce the number of false alarms. Experimental results on real datasets have demonstrated that we can effectively detect long-period alarms over a long time span. This can enhance the understanding of the overall network security situation and help staff pinpoint high-risk alarms with real threats.