Log Based Anomaly Detection

Log-based anomaly detection aims to automatically identify unusual system behaviors by analyzing system logs, improving system reliability and reducing downtime. Current research focuses on improving the effectiveness of deep learning models, such as transformers and graph neural networks, and exploring techniques to reduce data volume and improve model efficiency, including optimized traditional methods like PCA. This field is significant because effective anomaly detection is crucial for maintaining the stability and security of complex software systems, impacting both research in machine learning and practical applications in IT operations and system management.