Research Article
Anomaly Detection on Web-User Behaviors Through Deep Learning
@INPROCEEDINGS{10.1007/978-3-030-63086-7_25, author={Jiaping Gui and Zhengzhang Chen and Xiao Yu and Cristian Lumezanu and Haifeng Chen}, title={Anomaly Detection on Web-User Behaviors Through Deep Learning}, 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={Web application Abnormal behavior Sequence-based attack Deep learning}, doi={10.1007/978-3-030-63086-7_25} }- Jiaping Gui
Zhengzhang Chen
Xiao Yu
Cristian Lumezanu
Haifeng Chen
Year: 2020
Anomaly Detection on Web-User Behaviors Through Deep Learning
SECURECOMM
Springer
DOI: 10.1007/978-3-030-63086-7_25
Abstract
The modern Internet has witnessed the proliferation of web applications that play a crucial role in the branding process among enterprises. Web applications provide a communication channel between potential customers and business products. However, web applications are also targeted by attackers due to sensitive information stored in these applications. Among web-related attacks, there exists a rising but more stealthy attack where attackers first access a web application on behalf of normal users based on stolen credentials. Then attackers follow a sequence of sophisticated steps to achieve the malicious purpose. Traditional security solutions fail to detect relevant abnormal behaviors once attackers login to the web application. To address this problem, we proposeWebLearner, a novel system to detect abnormal web-user behaviors. As we demonstrate in the evaluation,WebLearnerhas an outstanding performance. In particular, it can effectively detect abnormal user behaviors with over 96% for both precision and recall rates using a reasonably small amount of normal training data.
