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Detecting and Explaining Anomalies Caused by Web Tamper Attacks via Building Consistency-based Normality

Year: 2024

Type: conference-paper

Abstract: 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.

Source: IEEE/ACM International Conference on Automated Software Engineering (ASE)

DOI: 10.1145/3691620.3695024

Authors: Yifan Liao, Ming Xu, Yun Lin, Xiwen Teoh, Xiaofei Xie, Ruitao Feng, Frank Liaw, Hongyu Zhang, Jin Song Dong

Institutions: National University of Singapore, Shanghai Jiao Tong University, Singapore Management University, Government Technology Agency, Chongqing University

Language: English

Topic: Software System Performance and Reliability

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4403536563

FWCI: 0.91

Cites: 36

Cited by: 2

Related to: 10