Anomaly Detection on Servers Using Log Analysis

Increasing data volume and complexity make log analysis mandatory for security and performance management in server systems. In this new era, where traditional manual methods are insufficient, the automatic log analysis potential of artificial intelligence and deep learning techniques comes to the fore. In this study, a deep learning model is developed to detect anomalies by analyzing log data collected from servers and devices. This log anomaly detection model, developed using a Convolutional Neural Network (CNN), uses structured log data processed with the Drain log parsing algorithm and effectively classifies anomalies by extracting features from this data. In the experimental studies conducted on Hadoop Distributed File System (HDFS) log data, it is observed that the model reaches up to $99 \%$ accuracy rates and improves both debugging processes and operating efficiency.