PacketDiff: A Flow Guided Diffusion Model for Network Packet Trace Generation

Shiyuan Zhang, Haoye Chai, Yong Li, Baohua Qiu, Lei Yue, Raoyuan Pan

Abstract: In large-scale Internet of Things (IoT) networks, generating high-fidelity packet trace data (e.g., packet size, packet time interval) is crucial for developing more powerful and precise network analysis tools. The packet data facilitates improved monitoring, threat detection, and the design of tailored resource allocation strategies to enhance user experiences. However, due to the inherent variability of IoT services and the complex temporal dependencies between network packets, producing detailed packet trace data for multiple services remains a challenging task. In this article, we propose PacketDiff, a novel diffusion model that generates network packet trace data for diverse services based on statistical network data (i.e., network flow data). We first design an IP graph to characterize and learn the preference relationships between different IoT devices in terms of service usage. Afterward, a classifier-free guidance denoising network that integrates network flow information is designed with dual-layer transformer architectures to enhance the controllability of the generation process. Unlike traditional methods, which often rely on the simple replication of statistical patterns within IoT traffic data and fail to account for the dynamic and varied nature of IoT networks, our approach offers a more robust and accurate solution. Extensive experiments conducted on two real-world IoT datasets demonstrate that PacketDiff closely approximates real network packet data, particularly concerning key characteristics, such as average packet size and time interval, with improvements of 43.4% and 39.02%.

External IDs:dblp:journals/iotj/ZhangCLQYP25