Practical GAN-based synthetic IP header trace generation using NetShare
Practical GAN-based synthetic IP header trace generation using NetShare
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DOI：
10.1145/3544216.3544251
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Yucheng Yin;Zinan Lin;Minhao Jin;G. Fanti;Vyas Sekar
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作者：
Yucheng Yin;Zinan Lin;Minhao Jin;G. Fanti;Vyas Sekar
我们探索了使用生成对抗网络（GAN）自动学习生成模型以生成网络任务（例如，遥测、异常检测、供应）。我们确定了现有基于GAN的方法中的关键保真度，可扩展性和隐私挑战以及权衡。通过将特定领域的见解与机器学习和隐私方面的最新进展相结合，我们确定了应对这些挑战的设计选择。基于这些见解，我们开发了一个端到端的框架，NetShare。我们在六个不同的数据包报头跟踪上评估了NetShare，发现：（1）在所有分布度量和跟踪中，它比基线高出46%的准确性，（2）它满足了用户在评估候选方法的准确性和排序方面对下游任务的要求。
We explore the feasibility of using Generative Adversarial Networks (GANs) to automatically learn generative models to generate synthetic packet- and flow header traces for networking tasks (e.g., telemetry, anomaly detection, provisioning). We identify key fidelity, scalability, and privacy challenges and tradeoffs in existing GAN-based approaches. By synthesizing domain-specific insights with recent advances in machine learning and privacy, we identify design choices to tackle these challenges. Building on these insights, we develop an end-to-end framework, NetShare. We evaluate NetShare on six diverse packet header traces and find that: (1) across all distributional metrics and traces, it achieves 46% more accuracy than baselines and (2) it meets users' requirements of downstream tasks in evaluating accuracy and rank ordering of candidate approaches.