a large dataset of 609,934 real Modbus packets
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a large dataset of 609,934 real Modbus packetsIn industrial control systems (ICS), the Modbus protocol remains a cornerstone for communication between field devices and supervisory systems. However, limited availability of diverse and realistic datasets for Modbus TCP traffic hinders the development of advanced cybersecurity solutions. In this study, we present a novel approach to synthesize Modbus TCP packet data using a Wasserstein Generative Adversarial Network (WGAN) augmented with Gumbel-Softmax sampling. Our system learns from a large dataset of 609,934 real Modbus packets, replicating their structure, byte distribution, and variability to produce synthetic yet protocol-valid packet data. We design and implement a generator-critic architecture, optimizing training with gradient penalties and leveraging PyTorch for scalability. Evaluation of the generated packets demonstrates a 93.92% Test System Reception Rate (TSRR) when interacting with a Modbus server, confirming their validity and realism. Furthermore, statistical comparisons between real and generated packet byte distributions reveal significant alignment, while diversity metrics and structural conformity ensure utility in anomaly detection and robustness testing. This synthetic data solution has promising implications for training intrusion detection systems (IDS) in resource-constrained or privacy-sensitive environments, providing a secure, scalable, and high-fidelity alternative to raw packet captures.
在工业控制系统（Industrial Control Systems, ICS）中，Modbus协议仍是现场设备与监控系统间通信的核心基石。然而，当前针对Modbus TCP流量的多样化、高真实性数据集资源匮乏，严重阻碍了先进网络安全解决方案的研发。本研究提出一种基于改进型Wasserstein生成对抗网络（Wasserstein Generative Adversarial Network, WGAN）结合Gumbel-Softmax采样的Modbus TCP数据包合成新方法。该模型依托规模达609,934条的真实Modbus数据包数据集进行训练，可复现真实数据包的结构、字节分布与变异性，生成符合协议规范的合成数据包。研究团队设计并实现了生成器-评判器架构，采用梯度惩罚策略优化训练过程，并依托PyTorch框架提升模型可扩展性。通过与Modbus服务器交互测试，合成数据包的测试系统接收率（Test System Reception Rate, TSRR）达93.92%，验证了其协议合规性与真实性。此外，对真实与合成数据包的字节分布进行统计对比后发现，二者具有高度一致性；同时，多样性指标与结构合规性测试结果表明，该合成数据可有效应用于异常检测与鲁棒性验证场景。该合成数据方案可为资源受限或隐私敏感场景下的入侵检测系统（Intrusion Detection System, IDS）训练提供新思路，相较于原始数据包捕获方案，其具备安全性更高、可扩展性更强、保真度更优的显著优势。
