Dynamic Object Language Labs

This paper describes an automated process of active perception for cyber defense. Our approach is informed by theoretical ideas from decision theory and recent research results in neuroscience. Our cognitive agent allocates computational and sensing resources to (approximately) optimize its Value of Information. To do this, it draws on models to direct sensors towards phenomena of greatest interest to inform decisions about cyber defense actions. By identifying critical network assets, the organization's mission measures interest (and value of information). This model enables the system to follow leads from inexpensive, inaccurate alerts with targeted use of expensive, accurate sensors. This allows the deployment of sensors to build structured interpretations of situations. From these, an organization can meet mission-centered decision-making requirements with calibrated responses proportional to the likelihood of true detection and degree of threat.

We outline our approach to developing, a distributed capability to achieve shared situation awareness of mission status and trust relationships, anticipate and diagnose cyber threats, and respond strategically and tactically to those threats.

Our goal is to lay a foundation for a model of high level cognition in humans that respects biological constraints. To do so, we integrate realistic neural models of attention, memory and control into an active information foraging system comprising many of the component neural systems and strategies in published models of high-level active vision. The direction of attentional focus requires integration of bottom-up and top-down attention. We base our modeling methodology for both individual components and the overall system operation on structured hierarchical Bayesian models. These models are built upon computationally tractable, neurally implementable sampling approximations to inference and control.