TempoNet: Learning Realistic Communication and Timing Patterns for Network Traffic Simulation

• URL: http://arxiv.org/abs/2601.15663v1
• Date: Thu, 22 Jan 2026 05:23:19 GMT
• Title: TempoNet: Learning Realistic Communication and Timing Patterns for Network Traffic Simulation
• Authors: Kristen Moore, Diksha Goel, Cody James Christopher, Zhen Wang, Minjune Kim, Ahmed Ibrahim, Ahmad Mohsin, Seyit Camtepe,
• Abstract summary: This paper introduces TempoNet, a novel generative model that combines multi-task learning with multi-mark temporal point processes.<n>We show that intrusion detection models trained on TempoNet-generated background traffic perform comparably to those trained on real data.
• Score: 10.446543382702837
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Realistic network traffic simulation is critical for evaluating intrusion detection systems, stress-testing network protocols, and constructing high-fidelity environments for cybersecurity training. While attack traffic can often be layered into training environments using red-teaming or replay methods, generating authentic benign background traffic remains a core challenge -- particularly in simulating the complex temporal and communication dynamics of real-world networks. This paper introduces TempoNet, a novel generative model that combines multi-task learning with multi-mark temporal point processes to jointly model inter-arrival times and all packet- and flow-header fields. TempoNet captures fine-grained timing patterns and higher-order correlations such as host-pair behavior and seasonal trends, addressing key limitations of GAN-, LLM-, and Bayesian-based methods that fail to reproduce structured temporal variation. TempoNet produces temporally consistent, high-fidelity traces, validated on real-world datasets. Furthermore, we show that intrusion detection models trained on TempoNet-generated background traffic perform comparably to those trained on real data, validating its utility for real-world security applications.

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