Multi-view Correlation-aware Network Traffic Detection on Flow Hypergraph

TL;DR

This paper introduces FlowID, a multi-view correlation-aware network traffic detection framework that leverages temporal, interaction, and hypergraph features, along with contrastive learning, to improve accuracy and robustness across diverse scenarios.

Contribution

The paper presents a novel multi-view framework with hypergraph encoding and contrastive learning to enhance network traffic detection beyond existing single-view methods.

Findings

FlowID outperforms existing methods in accuracy and robustness.

It demonstrates strong generalization across diverse network scenarios.

Effective in detecting malicious traffic with high precision.

Abstract

As the Internet rapidly expands, the increasing complexity and diversity of network activities pose significant challenges to effective network governance and security regulation. Network traffic, which serves as a crucial data carrier of network activities, has become indispensable in this process. Network traffic detection aims to monitor, analyze, and evaluate the data flows transmitted across the network to ensure network security and optimize performance. However, existing network traffic detection methods generally suffer from several limitations: 1) a narrow focus on characterizing traffic features from a single perspective; 2) insufficient exploration of discriminative features for different traffic; 3) poor generalization to different traffic scenarios. To address these issues, we propose a multi-view correlation-aware framework named FlowID for network traffic detection. FlowID captures multi-view traffic features via temporal and interaction awareness, while a hypergraph encoder further explores higher-order relationships between flows. To overcome the challenges of data imbalance and label scarcity, we design a dual-contrastive proxy task, enhancing the framework's ability to differentiate between various traffic flows through traffic-to-traffic and group-to-group contrast. Extensive experiments on five real-world datasets demonstrate that FlowID significantly outperforms existing methods in accuracy, robustness, and generalization across diverse network scenarios, particularly in detecting malicious traffic.