[HTML][HTML] Deep learning for anomaly detection in log data: A survey
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 …
Ai for it operations (aiops) on cloud platforms: Reviews, opportunities and challenges
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big
data generated by IT Operations processes, particularly in cloud infrastructures, to provide …
data generated by IT Operations processes, particularly in cloud infrastructures, to provide …
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 …
Rcagent: Cloud root cause analysis by autonomous agents with tool-augmented large language models
Large language model (LLM) applications in cloud root cause analysis (RCA) have been
actively explored recently. However, current methods are still reliant on manual workflow …
actively explored recently. However, current methods are still reliant on manual workflow …
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 …
BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data
M Ma, L Han, C Zhou - Advanced Engineering Informatics, 2023 - Elsevier
In the context of big data, if the task of multivariate time series data anomaly detection cannot
be performed efficiently and accurately, it will bring great security risks to industrial systems …
be performed efficiently and accurately, it will bring great security risks to industrial systems …
{AIRTAG}: Towards Automated Attack Investigation by Unsupervised Learning with Log Texts
The success of deep learning (DL) techniques has led to their adoption in many fields,
including attack investigation, which aims to recover the whole attack story from logged …
including attack investigation, which aims to recover the whole attack story from logged …
Lanobert: System log anomaly detection based on bert masked language model
The system log generated in a computer system refers to large-scale data that are collected
simultaneously and used as the basic data for determining errors, intrusion and abnormal …
simultaneously and used as the basic data for determining errors, intrusion and abnormal …
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 …