Machine learning enabled adaptive user interfaces for enhanced network security

机器学习支持自适应用户界面，以增强网络安全性

• 批准号: 499391-2016
• 负责人: Sanner, Scott
• 金额: $ 1.82万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=607398
• 关键词: Machine; learning; enabled; adaptive; user

Manual network monitoring is essential for network-security in the presence of increasingly sophisticated and novel forms of cyber-attack. Unfortunately, manually monitoring complex configurations of devices across different logical views of a computer network leads to an information overload when all information is visualized simultaneously. To this end, it is critical to build adaptive network visualization tools that anticipate users' needs with minimal interaction as well as triage potential network threats without overloading a user. At the core of such adaptivity are two tasks: (1) the need to perform fast inference in dynamical influence diagram models of system state and associated risks to inform interface adaptations and (2) the need to learn and continually update both the system model and the user model in order to ensure accurate inference and decision-making in part (1). We will partner with Uncharted to develop novel machine learning and inference algorithms to address both tasks (1) and (2) in a novel interface for the development of novel enhanced network security monitoring tools. While this research project will have direct application to cyber-security and network monitoring, the underlying techniques should generalize to a broad range of adaptive user interfaces.

在日益复杂和新颖的网络攻击形式下，手动网络监控对于网络安全至关重要。不幸的是，当所有信息同时可视化时，跨计算机网络的不同逻辑视图手动监视设备的复杂配置会导致信息过载。为此，构建自适应网络可视化工具至关重要，这些工具可以以最少的交互来预测用户的需求，并在不使用户过载的情况下对潜在的网络威胁进行分类。这种自适应性的核心是两个任务：（1）需要在系统状态和相关风险的动态影响图模型中执行快速推理，以通知接口适配，以及（2）需要学习和不断更新系统模型和用户模型，以确保部分（1）中的准确推理和决策。我们将与Uncharted合作开发新的机器学习和推理算法，以解决新界面中的任务（1）和（2），以开发新的增强型网络安全监控工具。虽然该研究项目将直接应用于网络安全和网络监控，但底层技术应该推广到广泛的自适应用户界面。