MLAD: A Unified Model for Multi-system Log Anomaly Detection
In spite of the rapid advancements in unsupervised log anomaly detection techniques, the
current mainstream models still necessitate specific training for individual system datasets …
current mainstream models still necessitate specific training for individual system datasets …
SaRLog: Semantic-Aware Robust Log Anomaly Detection via BERT-Augmented Contrastive Learning
Numerous deep learning-based methods have been developed to address the intricacies of
anomaly detection tasks within system logs, presenting two significant challenges. First …
anomaly detection tasks within system logs, presenting two significant challenges. First …
LogGT: Cross-system log anomaly detection via heterogeneous graph feature and transfer learning
P Wang, X Zhang, Z Cao, W Xu, W Li - Expert Systems with Applications, 2024 - Elsevier
Automated system log anomaly detection plays a crucial role in ensuring service reliability.
Existing methods incompletely utilize structured log entries, resulting in the loss of key …
Existing methods incompletely utilize structured log entries, resulting in the loss of key …
RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information
As the IT industry advances, system log data becomes increasingly crucial. Many computer
systems rely on log texts for management due to restricted access to source code. The need …
systems rely on log texts for management due to restricted access to source code. The need …
Auglog: System log anomaly detection based on contrastive learning and data augmentation
J Zhou, Y Qian - 2022 5th International Conference on Data …, 2022 - ieeexplore.ieee.org
System logs are valuable resources for system main-tenance and troubleshooting since they
record run-time status and significant events of computer systems. Detecting anomalies via …
record run-time status and significant events of computer systems. Detecting anomalies via …
Log-based anomaly detection without log parsing
Software systems often record important runtime information in system logs for
troubleshooting purposes. There have been many studies that use log data to construct …
troubleshooting purposes. There have been many studies that use log data to construct …
Lightweight Multi-task Learning Method for System Log Anomaly Detection
TA Pham, JH Lee - IEEE Access, 2024 - ieeexplore.ieee.org
Log anomaly detection is a crucial task in monitoring IT systems along with metrics and
traces. An anomaly could be detected by either one of two types of logs: individual logs or …
traces. An anomaly could be detected by either one of two types of logs: individual logs or …
RAGLog: Log Anomaly Detection using Retrieval Augmented Generation
J Pan, SL Wong, Y Yuan - arXiv preprint arXiv:2311.05261, 2023 - arxiv.org
The ability to detect log anomalies from system logs is a vital activity needed to ensure cyber
resiliency of systems. It is applied for fault identification or facilitate cyber investigation and …
resiliency of systems. It is applied for fault identification or facilitate cyber investigation and …
LogSD: Detecting Anomalies from System Logs through Self-supervised Learning and Frequency-based Masking
Log analysis is one of the main techniques that engineers use for troubleshooting large-
scale software systems. Over the years, many supervised, semi-supervised, and …
scale software systems. Over the years, many supervised, semi-supervised, and …
HEART: Heterogeneous log anomaly detection using robust transformers
Log sequences generated by heterogeneous systems are critical for understanding
computer system behaviour and ensuring operational and security integrity. However, the …
computer system behaviour and ensuring operational and security integrity. However, the …