Research on privacy data protection of power wireless network based on heterogeneous data sequence

As an energy industry, the power industry is related to the national economy and the people's livelihood. It is applied to intensive technology types, and involves all walks of life. Due to the opening of power market and the changes of social economy, such as Internet economy and data economy, the traditional power industry is facing great challenges. Considering the practical problem that the private data anomaly detection model of power network lacks high-quality labeled data set and the anomaly detector is updated in time according to the feedback, an adaptive anomaly detection model of human in the loop is constructed so as to effectively reduce the phenomenon of false positives. This paper proposes an adaptive anomaly detection algorithm based on isolated forest and prediction analysis. The abnormal score is calculated according to the algorithm. The sorting list of abnormal data is given. The samples to be labeled are selected by pal strategy and handed over to the analysts, and then the weight of each side in the database is updated in time according to the labeling feedback. The experimental results show that the detection accuracy of this algorithm is 95% under the low manual annotation budget, which is 5%-10% higher than other algorithms. Besides, about 70% abnormal behaviors were detected from 20000 test data. It realizes the purpose of identifying directional network attacks from multi-source heterogeneous and noisy cyberspace data. This research can help the power department quickly find the abnormal behavior caused by network attack, which aims to solve the problems of low intelligence level and lack of correlation between multi-source heterogeneous data in power network attack detection. (C) 2022 The Author(s). Published by Elsevier Ltd.