Enhancing Anomaly Detection in Large-Scale Log Data Using Machine Learning: A Comparative Study of SVM and KNN Algorithms with HDFS Dataset

English (OpenAlex)

As information technology rapidly advances, servers, mobile, and desktop applications are easily attacked due to their high value. Therefore, cyber attacks have raised great concerns in many areas. Anomaly detection plays a significant role in the field of cyber attacks, and log records, which record detailed system runtime information, have consequently become an important data analysis object. Traditional log anomaly detection relies on programmers manually inspecting logs through keyword searches and regular expression matching. While programmers can use intrusion detection systems to reduce their workload, log data is massive, attack types are diverse, and the advancement of hacking skills makes traditional detection inefficient. To improve traditional detection technology, many anomaly detection mechanisms, especially machine learning methods, have been proposed in recent years. In this study, an anomaly detection system using two different machine learning algorithms is proposed for large log data. Using Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) algorithms, experiments were conducted with the Hadoop Distributed File System (HDFS) log dataset, and experimental results show that this system provides higher detection accuracy and can detect unknown anomaly data.