Web applications are crucial infrastructures in the modern society, which have high demand of reliability and security. However, their frontend can be manipulable by the clients (e.g., the frontend code can be modified to bypass some validation steps), which incurs the runtime anomaly when operating the web service. Existing state-of-the-art anomaly detectors largely learn a deep learning model from the collected logs to predict abnormal logs with a probability. While effective in general, those approaches can suffer from (1) inaccuracy caused by subtle difference between the normal and abnormal/attack logs and (2) additional efforts for root cause analysis.