Work

Autoencoding-Based Self-Supervised Learning for Enhanced Representation of Network Traffic Patterns

Year: 2025

Type: article

Abstract: Network Traffic Monitoring and Analysis (NTMA) is crucial in today’s digital age, especially given the increasing volume of network data and associated cyber threats. Effective NTMA involves analyzing data packets for performance optimization, security, and policy compliance. However, traditional NTMA machine learning methods rely heavily on labeled data, which is costly and scarce. The objective of this work is to develop a self-supervised learning framework, VAE-SSL, that improves network traffic classification by leveraging unlabeled data. Specifically, we aim to enhance network traffic representation learning by designing novel pretext tasks tailored for tabular data, enabling better generalization across classification models. The VAE-SSL employs innovative pretext tasks tailored to tabular data, improving the performance of various classification models, including AdaBoost, SVC, Logistic Regression, and SGD. Our experiments demonstrate that VAE-SSL significantly improves classification metrics compared to traditional supervised learning approaches.

Source: IEEE Open Journal of the Communications Society

DOI: 10.1109/icmlcn64995.2025.11140475

Authors: Mahmoud Abbasi, Amin Shahraki, Marta Plaza-Hernandez, Juan Manuel Corchado, Javier Prieto

Institutions: Universidad de Salamanca, University of Oslo

Language: English

Topic: Internet Traffic Analysis and Secure E-voting

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075