internal-docs/papers/Practical GAN-based synthetic IP header trace generation using/Practical GAN-based synthetic IP header trace generation using.bib

@inproceedings{10.1145/3544216.3544251,
author = {Yin, Yucheng and Lin, Zinan and Jin, Minhao and Fanti, Giulia and Sekar, Vyas},
title = {Practical GAN-based synthetic IP header trace generation using NetShare},
year = {2022},
isbn = {9781450394208},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3544216.3544251},
doi = {10.1145/3544216.3544251},
abstract = {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.},
booktitle = {Proceedings of the ACM SIGCOMM 2022 Conference},
pages = {458–472},
numpages = {15},
keywords = {synthetic data generation, privacy, network packets, network flows, generative adversarial networks},
location = {Amsterdam, Netherlands},
series = {SIGCOMM '22}
}