Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

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 …

Software-intensive systems produce logs for troubleshooting purposes. Recently, many
deep learning models have been proposed to automatically detect system anomalies based …

Software systems often record important runtime information in system logs for
troubleshooting purposes. There have been many studies that use log data to construct …

In this work we systematically review the recent advancements in software engineering with
language models, covering 70+ models, 40+ evaluation tasks, 180+ datasets, and 900 …

Globally, the external internet is increasingly being connected to industrial control systems.
As a result, there is an immediate need to protect these networks from a variety of threats …

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 …

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 …

The increasing volume of log data produced by software-intensive systems makes it
impractical to analyze them manually. Many deep learning-based methods have been …

While deep learning (DL) has emerged as a powerful technique, its benefits must be
carefully considered in relation to computational costs. Specifically, although DL methods …