FIGAN: Diversity-Oriented Traffic Generation for Industrial Protocol Format Inference
发布时间：2026-09-13 点击次数：
姚羽（教授）
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- 博士生导师 硕士生导师
- 所在单位：计算机科学与工程学院
- 职务：复杂网络系统安全保障技术教育部工程研究中心主任
- 学历：博士研究生毕业
- 性别：男
- 联系方式：
- 学位：博士
- 毕业院校：东北大学
- 所属院系：计算机科学与工程学院
- 学科：
计算机应用技术
计算机软件与理论
计算机系统结构
- 论文名称：FIGAN: Diversity-Oriented Traffic Generation for Industrial Protocol Format Inference
- 发表刊物：IEEE Transactions on Network and Service Management
- 摘要：Protocol Format Inference is a pivotal step in the reverse engineering of proprietary protocols, yet its effectiveness is constrained by the scarcity of high-quality training data. In industrial control systems, the rigid and cyclical nature of traffic results in a “long-tail” distribution, where diverse functional scenarios are severely underrepresented. Existing generative approaches, primarily designed for fuzzing or intrusion detection, fail to resolve the intrinsic conflict between syntactic validity and semantic diversity required for protocol format inference. To bridge this gap, we propose FIGAN, a stage-wise decoupled generative framework tailored to synthesize high-fidelity traffic for protocol format inference. By isolating flexible distribution learning from rigid syntax enforcement, FIGAN liberates the generative process to extrapolate novel payload variations from a continuous latent space, effectively surmounting the limitations of sparse seed data. Specifically, the framework integrates three synergistic modules: first, heuristic pre-processing that constructs semantic templates as a prior knowledge base; second, a generative adversarial architecture optimized via discrete relaxation to explore high-dimensional payload patterns independently of syntax rules; and finally, a closed-loop verification mechanism that performs syntactic calibration and functional validation against simulated device responses. Evaluations on four real-world protocols (Modbus TCP, S7Comm, Omron FINS, and DNP3) demonstrate that FIGAN significantly outperforms state-of-the-art baselines.
- 关键字：Communication system traffic, data augmentation ,generative adversarial networks, industrial control, inference algorithms
- 论文类型：SCI JCR Q2
- 备注：https://xplorestaging.ieee.org/document/11626179
- 学科门类：工学
- 文献类型：JCR 一区
- 一级学科：计算机科学与技术
- 是否译文：否