RINGS: Enabling Data-Driven Innovation for Next-Generation Networks Via Synthetic Data

下一代网络系统越来越受数据驱动，这意味着它们是在真实数据上开发、调整和测试的。例如，数据驱动技术可以为互联网上的内容分发提供更好的体验质量，提供更好的无线通信技术，以及为新出现的网络安全威胁提供更好的攻击检测技术。不幸的是，普遍缺乏数据限制了数据驱动研究和开发的潜力。数据持有者通常不愿共享数据集，因为担心泄露商业秘密或违反法规。这些数据访问挑战将成为（并且已经是）下一代网络创新的根本绊脚石。该项目旨在通过合成数据解决这一僵局，合成数据显示与真实数据相同的统计模式，而不需要显式地共享原始源数据。合成数据集可以安全地发布，以实现跨利益相关者的协作。然而，合成数据生成技术通常存在数据质量差的问题。本提案探讨了如何利用和扩展机器学习的最新进展，以使用生成对抗网络（gan）来生成网络数据集的合成模型。然而，实现gan生成的网络系统合成数据的潜在优势在多个方面都具有挑战性。首先，网络流量数据集（例如，数据包捕获）需要复杂的关系，这对先前的GAN模型提出了新的保真度和可扩展性影响。其次，网络用例提出了新的（和传统的）隐私要求，由此产生的隐私-保真权衡仍然知之甚少。最后，一些网络用例需要研究罕见或极端事件（例如，中断、闪电人群、攻击）。对于gan（或任何合成数据模型）来说，这种极端事件的数据定义是罕见的，并且具有挑战性。该项目将解决跨越网络、机器学习和隐私的跨学科挑战，为支持下一代网络系统中数据驱动操作的gan工作流程开发新的基础。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Next-generation networked systems are increasingly data-driven, meaning they are developed, tuned, and tested on real data. For instance, data-driven techniques can enable better quality of experience for content distribution over the Internet, better wireless communication techniques, and better attack detection techniques for emerging cybersecurity threats. Unfortunately, a pervasive lack of data limits the potential of data-driven research and development. Data holders are often reluctant to share datasets for fear of revealing business secrets or running afoul of regulations. These data access challenges will become (and already are) a fundamental stumbling block for innovation in next-generation networks.This project aims to tackle this impasse with synthetic data —-- data that exhibits the same statistical patterns as real data, without the need to explicitly share the original source data. Synthetic datasets can be safely released to enable cross-stakeholder collaboration. Synthetic data generation techniques, however, have classically suffered from poor data quality. This proposal explores how to leverage and extend recent advances in machine learning to use Generative Adversarial Networks (GANs) to generate synthetic models of networking datasets. Realizing the potential benefits of GAN-generated synthetic data for networking systems, however, is challenging on multiple fronts. First, network traffic datasets (e.g., packet captures) entail complex relationships that raise new fidelity and scalability implications for prior GAN models. Second, networking use cases pose new (and traditional) privacy requirements, and the resulting privacy-fidelity tradeoffs remain poorly understood. Finally, several networking use cases entail studying rare or extreme events (e.g., outages, flash crowds, attacks). Data for such extreme events by definition is rare and challenging for GANs (or any synthetic data model) to learn. This project will tackle interdisciplinary challenges spanning networking, machine learning, and privacy to develop novel foundations for GAN-enabled workflows for supporting data-driven operations in next-generation network systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.