Title

Connecting web event forecasting with anomaly detection: A case study on enterprise web applications using self-supervised neural networks

Document Type

Conference Proceeding

Publication Date

12-12-2020

Department

College of Computing

Abstract

Recently web applications have been widely used in enterprises to assist employees in providing effective and efficient business processes. Forecasting upcoming web events in enterprise web applications can be beneficial in many ways, such as efficient caching and recommendation. In this paper, we present a web event forecasting approach, DeepEvent, in enterprise web applications for better anomaly detection. DeepEvent includes three key features: web-specific neural networks to take into account the characteristics of sequential web events, self-supervised learning techniques to overcome the scarcity of labeled data, and sequence embedding techniques to integrate contextual events and capture dependencies among web events. We evaluate DeepEvent on web events collected from six real-world enterprise web applications. Our experimental results demonstrate that DeepEvent is effective in forecasting sequential web events and detecting web based anomalies. DeepEvent provides a context-based system for researchers and practitioners to better forecast web events with situational awareness.

Publisher's Statement

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020. Publisher’s version of record: https://doi.org/10.1007/978-3-030-63086-7_27

Publication Title

Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

ISBN

9783030630850

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