Detection of anomalies in the data is an important data analysis job for server logs as they will reveal many benefits. Different types of methods can be used for anomaly detection: supervised, semi-supervised, and supervised anomaly detection. Similarly different algorithms exist for each category. In this work, four anomaly detection algorithms are utilized and their performance metrics are compared for public Hadoop Distributed File System (HDFS) data. Among the others, the support vector ...