Log anomaly detection method based on CNN and LSTM fusion

Jiahao Li; Zhuo Lv; Cen Chen

doi:10.1117/12.3024645

3 April 2024 Log anomaly detection method based on CNN and LSTM fusion

Jiahao Li, Zhuo Lv, Cen Chen

Proceedings Volume 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023); 1307818 (2024) https://doi.org/10.1117/12.3024645
Event: Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 2023, Wuhan, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiahao Li, Zhuo Lv, and Cen Chen "Log anomaly detection method based on CNN and LSTM fusion", Proc. SPIE 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 1307818 (3 April 2024); https://doi.org/10.1117/12.3024645

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