Work

AFF_CGE: Combined Attention-Aware Feature Fusion and Communication Graph Embedding Learning for Detecting Encrypted Malicious Traffic

Year: 2024

Type: article

Abstract: While encryption enhances data security, it also presents significant challenges for network traffic analysis, especially in detecting malicious activities. To tackle this challenge, this paper introduces combined Attention-aware Feature Fusion and Communication Graph Embedding Learning (AFF_CGE), an advanced representation learning framework designed for detecting encrypted malicious traffic. By leveraging an attention mechanism and graph neural networks, AFF_CGE extracts rich semantic information from encrypted traffic and captures complex relations between communicating nodes. Experimental results reveal that AFF_CGE substantially outperforms traditional methods, improving F1-scores by 5.3% through 22.8%. The framework achieves F1-scores ranging from 0.903 to 0.929 across various classifiers, exceeding the performance of state-of-the-art techniques. These results underscore the effectiveness and robustness of AFF_CGE in detecting encrypted malicious traffic, demonstrating its superior performance.

Source: Applied Sciences

DOI: 10.3390/app142210366

Authors: Junhao Liu, Guolin Shao, Hong Rao, Xiangjun Li, Xuan Huang

Institutions: Nanchang University, Jiangxi University of Finance and Economics

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,728