A comprehensive survey on graph anomaly detection with deep learning
Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …
the others in the sample. Over the past few decades, research on anomaly mining has …
[HTML][HTML] Deep learning for anomaly detection in log data: A survey
M Landauer, S Onder, F Skopik… - Machine Learning with …, 2023 - Elsevier
Automatic log file analysis enables early detection of relevant incidents such as system
failures. In particular, self-learning anomaly detection techniques capture patterns in log …
failures. In particular, self-learning anomaly detection techniques capture patterns in log …
Log-based anomaly detection with deep learning: How far are we?
Software-intensive systems produce logs for troubleshooting purposes. Recently, many
deep learning models have been proposed to automatically detect system anomalies based …
deep learning models have been proposed to automatically detect system anomalies based …
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 …
Log parsing with prompt-based few-shot learning
Logs generated by large-scale software systems provide crucial information for engineers to
understand the system status and diagnose problems of the systems. Log parsing, which …
understand the system status and diagnose problems of the systems. Log parsing, which …
Eadro: An end-to-end troubleshooting framework for microservices on multi-source data
The complexity and dynamism of microservices pose significant challenges to system
reliability, and thereby, automated troubleshooting is crucial. Effective root cause localization …
reliability, and thereby, automated troubleshooting is crucial. Effective root cause localization …
LightLog: A lightweight temporal convolutional network for log anomaly detection on the edge
Log anomaly detection on edge devices is the key to enhance edge security when
deploying IoT systems. Despite the success of many newly proposed deep learning based …
deploying IoT systems. Despite the success of many newly proposed deep learning based …
AutoLog: Anomaly detection by deep autoencoding of system logs
The use of system logs for detecting and troubleshooting anomalies of production systems
has been known since the early days of computers. In spite of the advances in the area, the …
has been known since the early days of computers. In spite of the advances in the area, the …
Face it yourselves: An llm-based two-stage strategy to localize configuration errors via logs
Configurable software systems are prone to configuration errors, resulting in significant
losses to companies. However, diagnosing these errors is challenging due to the vast and …
losses to companies. However, diagnosing these errors is challenging due to the vast and …
[PDF][PDF] Anomaly Detection in the Open World: Normality Shift Detection, Explanation, and Adaptation.
Concept drift is one of the most frustrating challenges for learning-based security
applications built on the closeworld assumption of identical distribution between training and …
applications built on the closeworld assumption of identical distribution between training and …