Shiming He¹, Tuo Deng¹, Bowen Chen¹, R. Simon Sherratt², Jin Wang^1,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 517-541, 2023, DOI:10.32604/cmc.2023.037392

Abstract Log anomaly detection is an important paradigm for system troubleshooting. Existing log anomaly detection based on Long Short-Term Memory (LSTM) networks is time-consuming to handle long sequences. Transformer model is introduced to promote efficiency. However, most existing Transformer-based log anomaly detection methods convert unstructured log messages into structured templates by log parsing, which introduces parsing errors. They only extract simple semantic feature, which ignores other features, and are generally supervised, relying on the amount of labeled data. To overcome the limitations of existing methods, this paper proposes a novel unsupervised log anomaly detection method based on multi-feature (UMFLog). UMFLog includes… More >

Feilu Hang¹, Wei Guo¹, Hexiong Chen¹, Linjiang Xie¹, Chenghao Zhou^2,*, Yao Liu²

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 517-529, 2023, DOI:10.32604/cmes.2023.025774

Abstract Modern large-scale enterprise systems produce large volumes of logs that record detailed system runtime status and key events at key points. These logs are valuable for analyzing performance issues and understanding the status of the system. Anomaly detection plays an important role in service management and system maintenance, and guarantees the reliability and security of online systems. Logs are universal semi-structured data, which causes difficulties for traditional manual detection and pattern-matching algorithms. While some deep learning algorithms utilize neural networks to detect anomalies, these approaches have an over-reliance on manually designed features, resulting in the effectiveness of anomaly detection depending… More >

Yong Fang, Zhiying Zhao, Yijia Xu^*, Zhonglin Liu

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4099-4118, 2023, DOI:10.32604/cmc.2023.033124

Abstract System logs are essential for detecting anomalies, querying faults, and tracing attacks. Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection, it cannot meet the actual needs. The implementation of automated log anomaly detection is a topic that demands urgent research. However, the prior work on processing log data is mainly one-dimensional and cannot profoundly learn the complex associations in log data. Meanwhile, there is a lack of attention to the utilization of log labels and usually relies on a large number of labels for detection. This paper proposes a novel and practical detection model… More >

Jin Wang¹, Changqing Zhao¹, Shiming He^1,*, Yu Gu², Osama Alfarraj³, Ahed Abugabah⁴

Computer Systems Science and Engineering, Vol.41, No.3, pp. 1207-1222, 2022, DOI:10.32604/csse.2022.022365

Abstract System logs record detailed information about system operation and are important for analyzing the system's operational status and performance. Rapid and accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more and more complex, and the number of system logs gradually increases, which brings challenges to analyze system logs. Some recent studies show that logs can be unstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a long time to train models. Therefore, to reduce the computational… More >

Xiaoyu Duan¹, Shi Ying^1,*, Wanli Yuan¹, Hailong Cheng¹, Xiang Yin²

Computer Systems Science and Engineering, Vol.37, No.1, pp. 135-148, 2021, DOI:10.32604/csse.2021.014030

Abstract Detecting anomaly logs is a great significance step for guarding system faults. Due to the uncertainty of abnormal log types, lack of real anomaly logs and accurately labeled log datasets. Existing technologies cannot be enough for detecting complex and various log point anomalies by using human-defined rules. We propose a log anomaly detection method based on Generative Adversarial Networks (GAN). This method uses the Encoder-Decoder framework based on Long Short-Term Memory (LSTM) network as the generator, takes the log keywords as the input of the encoder, and the decoder outputs the generated log template. The discriminator uses the Convolutional Neural… More >