Dataset for the ICSE'22 paper: Log-based Anomaly Detection with Deep Learning: How Far Are We? If you find the data useful for your research, please cite the following paper: @inproceedings{le2022log, title={Log-based anomaly detection with deep learning: How far are we?}, author={Le, Van-Hoang and Zhang, Hongyu}, booktitle={Proceedings of the 44th international conference on software engineering}, pages={1356--1367}, year={2022}
By using machine learning to monitor and find deviations in log data makes it easier for developers ...
Anomaly detection has been used to detect and analyze anomalous elements from data for years. Variou...
Modern enterprise IT systems generate large amounts of log data to record system state, potential er...
Software systems log massive amounts of data, recording important runtime information. Such logs ar...
The growth of systems complexity increases the need of automated techniques dedicated to different ...
As the complexity of today’s systems increases, manual system monitoring and log fi\u80le analysis a...
Artificial Intelligence for IT Operations (AIOps) describes the process of maintaining and operating...
Anomaly detection is a huge fi\u80eld of research focused on the task of \u80finding weird or outlyi...
Background: A problematic area in today’s large scale distributed systems is the exponential amount ...
Context: Log files are produced in most larger computer systems today which contain highly valuable ...
Log data is an important clue to understanding the behaviour of a system at runtime, but the complex...
Anomaly detection identifies unusual patterns or items in a dataset. The anomalies identified for sy...
The article analyzes the paths and algorithms for automating the monitoring of computer system state...
The rapid growth of deep learning (DL) has spurred interest in enhancing log-based anomaly detection...
In this tutorial we aim to present a comprehensive survey of the advances in deep learning technique...
By using machine learning to monitor and find deviations in log data makes it easier for developers ...
Anomaly detection has been used to detect and analyze anomalous elements from data for years. Variou...
Modern enterprise IT systems generate large amounts of log data to record system state, potential er...
Software systems log massive amounts of data, recording important runtime information. Such logs ar...
The growth of systems complexity increases the need of automated techniques dedicated to different ...
As the complexity of today’s systems increases, manual system monitoring and log fi\u80le analysis a...
Artificial Intelligence for IT Operations (AIOps) describes the process of maintaining and operating...
Anomaly detection is a huge fi\u80eld of research focused on the task of \u80finding weird or outlyi...
Background: A problematic area in today’s large scale distributed systems is the exponential amount ...
Context: Log files are produced in most larger computer systems today which contain highly valuable ...
Log data is an important clue to understanding the behaviour of a system at runtime, but the complex...
Anomaly detection identifies unusual patterns or items in a dataset. The anomalies identified for sy...
The article analyzes the paths and algorithms for automating the monitoring of computer system state...
The rapid growth of deep learning (DL) has spurred interest in enhancing log-based anomaly detection...
In this tutorial we aim to present a comprehensive survey of the advances in deep learning technique...
By using machine learning to monitor and find deviations in log data makes it easier for developers ...
Anomaly detection has been used to detect and analyze anomalous elements from data for years. Variou...
Modern enterprise IT systems generate large amounts of log data to record system state, potential er...