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AdvTG: An Adversarial Traffic Generation Framework to Deceive DL-Based Malicious Traffic Detection Models

Year: 2025

Type: conference-paper

Abstract: Deep learning-based (DL-based) malicious traffic detection models are effective but vulnerable to adversarial attacks. Existing adversarial attacks have shown promising results when targeting traffic detection models based on statistics and sequence features. However, these attacks are less effective against models that rely on payload analysis. The main reason is the difficulty in generating semantic, compliant, and functional payloads, which limits their practical application.

DOI: 10.1145/3696410.3714876

Authors: Peishuai Sun, Xiaochun Yun, Shuhao Li, Tao Yin, Chengxiang Si, Jiang Xie

Institutions: Chinese Academy of Sciences, Institute of Information Engineering, University of Chinese Academy of Sciences, National Computer Network Emergency Response Technical Team/Coordination Center of Chinar

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4409671228

FWCI: 10.82

Cites: 46

Cited by: 11

Related to: 10