Research Article
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-supervised Neural Networks
@INPROCEEDINGS{10.1007/978-3-030-63086-7_27, author={Xiaoyong Yuan and Lei Ding and Malek Ben Salem and Xiaolin Li and Dapeng Wu}, title={Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-supervised Neural Networks}, proceedings={Security and Privacy in Communication Networks. 16th EAI International Conference, SecureComm 2020, Washington, DC, USA, October 21-23, 2020, Proceedings, Part I}, proceedings_a={SECURECOMM}, year={2020}, month={12}, keywords={Anomaly detection Event forecasting Self-supervised learning Neural networks}, doi={10.1007/978-3-030-63086-7_27} }- Xiaoyong Yuan
Lei Ding
Malek Ben Salem
Xiaolin Li
Dapeng Wu
Year: 2020
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-supervised Neural Networks
SECURECOMM
Springer
DOI: 10.1007/978-3-030-63086-7_27
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.DeepEventincludes 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 evaluateDeepEventon web events collected from six real-world enterprise web applications. Our experimental results demonstrate thatDeepEventis effective in forecasting sequential web events and detecting web based anomalies.DeepEventprovides a context-based system for researchers and practitioners to better forecast web events with situational awareness.
