Cyber Situation Awareness through Instance-Based Learning

Abstract

In a corporate network, the situation awareness (SA) of a security analyst is of particular interest. The current work describes a cognitive Instance-Based Learning (IBL) model of an analyst’s recognition and comprehension processes in a cyber-attack scenario. The IBL model first recognizes network events based upon events’ situation attributes and their similarity to past experiences (instances) stored in the model’s memory. Then, the model comprehends a sequence of observed events as being a cyber-attack or not, based upon instances retrieved from its memory, similarity mechanism used, and the model’s risk-tolerance. The execution of the model generates predictions about the recognition and comprehension processes of an analyst in a cyber-attack. A security analyst’s decisions in the model are evaluated based upon two cyber-SA metrics of accuracy and timeliness. The chapter highlights the potential of this research for design of training and decision support tools for security analysts.