Visible to the public Model-Based Explanation For Human-in-the-Loop SecurityConflict Detection Enabled

Project Details

Lead PI

Performance Period

Nov 27, 2021

Institution(s)

Carnegie Mellon University

Sponsor(s)

National Security Agency

Ranked 35 out of 118 Group Projects in this group.
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Effective response to security attacks often requires a combination of both automated and human-mediated actions. Currently we lack adequate methods to reason about such human-system coordination, including ways to determine when to allocate tasks to each party and how to gain assurance that automated mechanisms are appropriately aligned with organizational needs and policies. In this project, we develop a model-based approach to (a) reason about when and how systems and humans should cooperate with each other, (b) improve human understanding and trust in automated behavior through self-explanation, and (c) provide mechanisms for humans to correct a system's automated behavior when it is inappropriate. We will explore the effectiveness of the techniques in the context of coordinated system-human approaches for mitigating advanced persistent threats (APTs).

Building on prior work that we have carried out in this area, we will show how probabilistic models and model checkers can be used both to synthesize complex plans that involve a combination of human and automated actions, as well as to provide human understandable explanations of mitigation plans proposed or carried out by the system. Critically, these models capture an explicit value system (in a multi-dimensional utility space) that forms the basis for determining courses of action. Because the value system is explicit we believe that it will be possible to provide a rational explanation of the principles that led to a given system plan. Moreover, our approach will allow the user to make corrective actions to that value system (and hence, future decisions) when it is misaligned. This will be done without a user needing to know the mathematical form of the revised utility reward function.