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

Abstract

Network Traffic Monitoring and Analysis (NTMA) is increasingly important given the growing volume of network data and the associated cyber threats. Effective NTMA involves analyzing data packets for performance optimization, security, and policy compliance. In recent years, Machine Learning (ML) has shown high performance in this domain; however, traditional ML methods rely heavily on labeled data, which is costly and scarce. This paper proposes AE-SSL (Autoencoding-Based Self-Supervised Learning), a denoising autoencoder framework that enhances the representation of IoT-centric network traffic patterns using unlabeled data. AE-SSL employs two complementary pretext tasks tailored to tabular data (binary mask prediction and corrupted-feature reconstruction), and improves the performance of various classification models, including traditional and deep learning-based models. In our IoT-centric evaluation on two datasets, AE-SSL significantly improves classification metrics compared to supervised learning approaches. For example, on the ACI IoT 2023 dataset (100 pretraining epochs), AE-SSL improves AdaBoost macro-precision from 70.3% to 83.0% and SVC accuracy from 86.0% to 96.4%. On the CICIoT 2023 dataset (100 epochs), AdaBoost accuracy improves from 95.1% to 96.9%, and Logistic Regression accuracy from 73.7% to 76.2%. Additionally, an ablation study on key parameters reveals their impact on model performance, providing insights into optimizing self-supervised learning for network traffic analysis. These findings point to the potential of self-supervised techniques for IoT-centric NTMA; generalization to enterprise, mobile, and cloud traffic remains to be established and is left to future work.