Web Access Log Anomaly Detection Based on Deep Learning
Quan Liu · 2021
Network information security is becoming a vital topic for many companies and research institutes with big data. Information leakage via cyber is rising rapidly. Many companies and research institutes managers have not realized the significance of information security. This paper describes an investigation of anomaly detection. We measure the format and content of the original web access log. The intent is to analyze types of access and distribution of access types of each user and to transform them into the type that can be learned by the machine. With the transformed log, a neural network model can be put in place to learn how to detect an abnormal access log and alert the relative computer managers. The main goal is to design a model that can assist computer managers to do the anomaly detection job.