关键词: 关键词：智能电网, 异常检测, 进程日志, 深度学习, 主题特征异常检测

Abstract: Abstract: With the expansion of smart grid systems， the process log data has grown exponentially， making traditional log anomaly detection methods inadequate for handling such massive and complex data volumes. To promptly detect abnormal processes in smart grid systems and ensure their safe and stable operation， this paper proposes LogLDAIDF， a process log anomaly detection method based on LDA-IDF. The method first extracts LDA topic features from process log sequences， then introduces the IDF （Inverse Document Frequency） method to weight the topic features， and selects the top-k weighted topics as the final process log topic features， finally combining with a Bi-LSTM deep learning model for process log anomaly detection. Experimental results on two real-world datasets， HDFS and OpenStack， demonstrate that our proposed method significantly outperforms existing methods， achieving F1 scores of 0.987 and 0.969， respectively， results show its effectiveness and practicality in process log anomaly detection.

Key words: Key words: smart grid, anomaly detection, process log, deep learning, semantic feature