Log Data Anomaly Detection Using a Machine Learning Model

In computing, a log file records events in an operating system or other software runs or messages between different users of communication software. In the simplest case, the system writes messages to a single log file. The traditional way of inspection of logs could prove to be less efficient and time-consuming, impacting productivity. This paper explores various machine learning algorithms and an autoencoder to detect anomalies that can help developers quickly identify and derive relevant and appropriate information from the logs maintained.