Log anomaly detection method based on CNN and LSTM fusion

Abstract

Timely detection of abnormal behavior in power monitoring systems is crucial for system stability. Traditional log anomaly analysis methods have limitations, especially in handling multi-source or multi-dimensional log data. This paper introduces a method named CLSTMlog, which combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks, making the most of CNN's ability to extract local features and LSTM's capability to handle temporal relationships. Experimental results demonstrate that CLSTMlog exhibits a significant improvement in performance on the HDFS and IoT-23 datasets, including higher accuracy and reliability. This method has the potential to enhance the efficiency of log anomaly detection in power monitoring systems and overall system performance.