Situation Projection for enhanced Human-Machine Interaction based on Rule Mining

An in-depth comprehension of the current situation stands as a critical factor in ensuring the effectiveness and safety of human-machine systems. This paper introduces an innovative approach centered around projecting the current situation into the near future, aiming to bolster overall Situation Awareness (SA). This proactive projection not only anticipates potential developments but also enhances the preceding perception and comprehension phases, thereby elevating human SA. The approach employs rules to forecast the situation's evolution derived from past experiences, contributing to an enriched user experience and efficiency in critical human-machine interaction systems. Through a case study in the realm of cybersecurity, we illustrate how this approach can predict the next moves of an attacker, directing the attention of security human operators to the most critical security events occurring in the monitored network, thus improving the SA levels. The projection technique has been integrated into a network security monitoring dashboard. This dashboard dynamically adjusts displayed information based on ongoing events and projected situations, further augmenting operators' SA.