Dynamic Network Traffic Identification with Scalable and Resilient Machine Learning

通过可扩展且有弹性的机器学习进行动态网络流量识别

• 批准号: 576922-2022
• 负责人: Liang, BenB
• 金额: $ 2.19万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751708
• 关键词: Dynamic; Network; Traffic; Identification; Scalable

With the emergence of data-hungry applications, such as big-data AI, augmented reality, wireless multimedia, and Internet-of-Things (IoT), the current generation of network infrastructure will face severe challenges in its struggle to satisfy the exploding service demands. In order to efficiently allocate the available networking resources among these diverse set of applications, traffic classification has become an essential technology for the optimal operation of a network. Moreover, the ever-changing characteristics of new applications demand a flexible and automatic approach. In response to these challenges, and in collaboration with TELUS, we propose a new research project to develop dynamic machine learning (ML) techniques to classify the network traffic of an Internet service provider (ISP). Our long-term objective is to understand the intersection between ML and networked systems and discover new theories and practical ML-based designed guidelines for ISPs. In the shorter term of this project, we focus on (1) developing scalable ML-based designs for dynamic network traffic classification, and (2) designing resilient ML algorithms for robust network traffic classification. Our scientific discoveries will contribute to the fundamental understanding and insights into future network services, and they will provide guidelines for future regulatory policies. Furthermore, through research collaboration and technology transfer, our technical outcomes benefit TELUS and its technology and business partners in Canada, to improve their innovation portfolio, system design, and profitability. This will help maintain their competitive advantage in the global market for the benefit of all Canadians.

随着大数据人工智能、增强现实、无线多媒体、物联网（IoT）等数据密集型应用的出现，当前一代网络基础设施将面临严峻的挑战，难以满足爆炸式的服务需求。为了在这些不同的应用之间有效地分配可用的网络资源，流分类已经成为网络优化运行的一项重要技术。此外，新应用程序不断变化的特性需要灵活和自动的方法。为了应对这些挑战，我们与TELUS合作，提出了一个新的研究项目，开发动态机器学习（ML）技术，以对互联网服务提供商（ISP）的网络流量进行分类。我们的长期目标是了解机器学习和网络系统之间的交集，并为互联网服务提供商发现新的理论和实用的基于机器学习的设计指南。在这个项目的短期内，我们专注于(1)为动态网络流量分类开发可扩展的基于ML的设计，以及(2)为鲁棒网络流量分类设计弹性ML算法。我们的科学发现将有助于对未来网络服务的基本理解和洞察，并将为未来的监管政策提供指导。此外，通过研究合作和技术转让，我们的技术成果使TELUS及其在加拿大的技术和业务合作伙伴受益，从而改善他们的创新组合、系统设计和盈利能力。这将有助于保持他们在全球市场上的竞争优势，造福所有加拿大人。