Adaptive level of detail displays based on machine reasoning methods

网络安全在许多领域日益受到关注，包括民用和国防、零售、工业运营和配电网络。全球每年因网络攻击造成的平均损失估计为每家公司770万美元，仅恶意软件每年的损害总额就超过5000亿美元。为了保护系统免受新的和日益复杂的攻击，由操作员手动监控网络仍然是一项基本的、但困难的、对认知构成挑战的任务。这项研究提出了FocalPoint，这是一种自适应用户界面(AUI)，使运营商能够更有效地监控敏感的网络系统，从而保护数据和关键网络基础设施。与现有的用户界面不同，FocalPoint跟踪用户交互行为--如平移、缩放和点击--并自动执行视觉调整，以唤起不同任务的适当注意力水平。这些适应性是使用以下两种方法的组合得出的：(1)允许FocalPoint推断用户的任务和注意力状态的计算认知模型，以及(2)使FocalPoint能够从用户和专家交互历史中学习的新型可扩展机器学习算法。最终的效果是，FocalPoint提供了针对每个用户和任务的个性化调整，这些调整将塑造用户的认知状态，以满足他们正在执行的任务的需求。尽管FocalPoint将主要为网络安全操作员开发，但该研究计划将产生通用的AUI原则和技术，即通用机器学习和推理方法，以及可跨域移植的可视界面设计原则。因此，我们预计这项研究将在各种应用领域培养未来的自适应用户界面。

Cyber-security is an increasing concern in many domains including civil and national defence, retail, industrial operations and power distribution networks. Worldwide annual average losses due to cyber-attacks are estimated at USD $7.7 million per company and total annual malware damages alone exceed USD $500 billion. To protect systems from novel and increasingly sophisticated attacks, manual network monitoring by human operators remains an essential, but difficult and cognitively-challenging task. This research proposes Focalpoint, an adaptive user interface (AUI) that enables operators to monitor sensitive cyber systems more effectively, thereby securing data and critical network infrastructure. In contrast to existing user-interfaces, Focalpoint tracks user interaction behaviors-such as pans, zooms and clicks-and automatically performs visual adaptations to evoke the appropriate attention level across different tasks. These adaptations are derived using a combination of (1) computational cognitive models that allow Focalpoint to infer the user's task and attentional state, and (2) novel scalable machine learning algorithms that enable Focalpoint to learn from user and expert interaction histories. The end effect is that Focalpoint delivers personalized adaptations that are tailored for each individual user and task that will shape the user's cognitive state to the demands of the task that they are performing. Although Focalpoint will be developed primarily for cyber-security operators, this research program will yield general AUI principles and techniques, i.e., general-purpose machine learning and inference methods, and visual interface design principles that are transferable across domains. As such, we envision that this research will foster future adaptive user interfaces in a variety of application areas.