Accurate Decentralized Application Identification via Encrypted Traffic Analysis Using Graph Neural Networks

Abstract

Decentralized Applications (DApps) are increasingly developed and deployed on blockchain platforms such as Ethereum. DApp fingerprinting can identify users’ visits to specific DApps by analyzing the resulting network traffic, revealing much sensitive information about the users, such as their real identities, financial conditions and religious or political preferences. DApps deployed on the same platform usually adopt the same communication interface and similar traffic encryption settings, making the resulting traffic less discriminative. Existing encrypted traffic classification methods either require hand-crafted and fine-tuning features or suffer from low accuracy. It remains a challenging task to conduct DApp fingerprinting in an accurate and efficient way. In this paper, we present GraphDApp, a novel DApp fingerprinting method using Graph Neural Networks (GNNs). We propose a graph structure named Traffic Interaction Graph (TIG) as an information-rich representation of encrypted DApp flows, which implicitly reserves multiple dimensional features in bidirectional client-server interactions. Using TIG, we turn DApp fingerprinting into a graph classification problem and design a powerful GNN-based classifier. We collect real-world traffic datasets from 1,300 DApps with more than 169,000 flows. The experimental results show that GraphDApp is superior to the other state-of-the-art methods in terms of classification accuracy in both closed- and open-world scenarios. In addition, GraphDApp maintains its high accuracy when being applied to the traditional mobile application classification.