A Survey on Log Anomaly Detection using Deep Learning

Logs generated from the security systems, network devices, servers, and various software applications are one of the ways to record the operational happening of the equipment or software. These logs are assets for extracting meaningful information related to system behavior. Increasing usage of computer devices and the evolution of software systems can be considered as one of triggering acts for the concentration on the analysis of logs. Also, considering the massive volume of unstructured data, it raises the requirement for automatic analysis of these logs. The log analysis is helpful for understanding system behavior, malfunctioning detection, security scanning, and failure prediction. Machine learning(ML) and Deep Learning (DL) methods have been proved potent tools for data classification problems and have been applied to various fields of research. The purpose of this survey is to review recent research on log anomaly detection using Deep Neural Networks. Survey also presents the brief of log parsing approaches, types of datasets used for log analysis, and various concepts proposed for Log Anomaly detection.