Zhanyang Xu^*, Zhe Wang, Jian Xu, Hongyan Shi, Hong Zhao

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3991-4015, 2024, DOI:10.32604/cmc.2024.051620 - 12 September 2024

Abstract System logs, serving as a pivotal data source for performance monitoring and anomaly detection, play an indispensable role in assuring service stability and reliability. Despite this, the majority of existing log-based anomaly detection methodologies predominantly depend on the sequence or quantity attributes of logs, utilizing solely a single Recurrent Neural Network (RNN) and its variant sequence models for detection. These approaches have not thoroughly exploited the semantic information embedded in logs, exhibit limited adaptability to novel logs, and a single model struggles to fully unearth the potential features within the log sequence. Addressing these challenges,… More >

Mingguang Yu^1,2, Xia Zhang^1,2,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2975-2994, 2023, DOI:10.32604/csse.2023.037505 - 03 April 2023

Abstract

In order to obtain information or discover knowledge from system logs, the first step is to perform log parsing, whereby unstructured raw logs can be transformed into a sequence of structured events. Although comprehensive studies on log parsing have been conducted in recent years, most assume that one event object corresponds to a single-line message. However, in a growing number of scenarios, one event object spans multiple lines in the log, for which parsing methods toward single-line events are not applicable. In order to address this problem, this paper proposes an automated log parsing method for

… 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 - 31 October 2022

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… More >