NFT-AF: Multi-Dimensional Non-Fungible Token Application Fingerprinting Over Encrypted Tunnels

Abstract

With the development of the blockchain economy, many criminals are attempting to profit from Non-Fungible Tokens (NFTs). Under the cover of encrypted tunnels, efficiently identifying NFT application traffic has become a challenge. Moreover, NFT applications exhibit a high degree of similarity in communication interfaces, traffic encryption settings, and even business behaviors, making it difficult to distinguish the traffic of different NFT applications. The fact that a regulator can determine the users of a website’s visit by fingerprinting encrypted traffic patterns brings hope for solving this challenge. However, existing Deep Learning (DL) fingerprinting requires complete flows for identification, leading to high training costs and identification latency. This paper presents a novel NFT application fingerprinting method over encrypted tunnels called NFT-AF (NFT Application Fingerprinting). It extracts encrypted tunnel, statistical, and sequential features from early-stage network traffic to generate multi-dimensional fingerprints. Then, it uses a lightweight random forest as the classifier to efficiently identify NFT applications. Our experimental results demonstrate that NFT-AF effectively identifies NFT applications over encrypted tunnels using early-stage session packets and outperforms the state-of-the-art (SOTA) methods.