Adaptive level of detail displays based on machine reasoning methods

点击翻译按钮获取中文摘要

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. **