Building scalable and real-time deep learning classification of encrypted network traffic

构建加密网络流量的可扩展且实时的深度学习分类

• 批准号: 543552-2019
• 负责人: Shafiq, MuhammadOmair
• 金额: $ 1.38万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=676992
• 关键词: Building; scalable; real; time; deep

With increasing use of cybersecurity-based solutions, most data transmitted over networks is secured either by encryption or similar techniques. While this is essential for security and the protection of data, network traffic classification remains challenging. This is further convoluted when large-scale data is generated at high speed. This requires classification of encrypted network traffic data that is capable of (1) obviating deep packet or content inspection, and (2) maintaining high accuracy on big data produced at high speed, both of which will be addressed by the proposed project. Deep learning is part of machine learning that focuses on learning data representations and is inspired by the structures and functions of the human brain known as artificial neural networks. We will lay the foundation of a framework that will be based on machine learning and deep learning to perform efficient classification of encrypted network traffic data. The outcomes from this research will be used by the industry partner as potential enhancements and extensions to their current products and for developing new products.

随着越来越多地使用基于网络安全的解决方案，通过网络传输的大多数数据都通过加密或类似技术进行保护。虽然这对数据的安全和保护至关重要，但网络流量分类仍然具有挑战性。当高速生成大规模数据时，这一点更加复杂。这需要对加密的网络流量数据进行分类，该分类能够(1)避免深度分组或内容检查，以及(2)对高速产生的大数据保持高精度，这两个问题都将由拟议的项目解决。深度学习是机器学习的一部分，专注于学习数据表示法，灵感来自于被称为人工神经网络的人脑的结构和功能。我们将为基于机器学习和深度学习的框架奠定基础，以执行加密网络流量数据的有效分类。这项研究的结果将被行业合作伙伴用作其现有产品的潜在增强和扩展，并用于开发新产品。