Log Anomaly Detection

Log anomaly detection aims to automatically identify unusual patterns in system logs, crucial for proactive system maintenance and security. Current research emphasizes developing efficient and accurate models, often leveraging deep learning architectures like Transformers and Graph Neural Networks, as well as semi-supervised and unsupervised learning techniques to address data scarcity and improve generalization across diverse systems. These advancements are significantly improving the speed and accuracy of anomaly detection, leading to more robust and reliable IT operations and reducing the burden on human operators.