AIMEE: Artificial Intelligence Mitigations of Emergent Execution

Modern computing systems demonstrate strong propensity for unintended, emergent computations and the related unintended, emergent programming models that enable or amplify cyber-attacks. 

Computing mechanisms built for a particular purpose and with particular intended models of execution in mind prove to be capable of executing unintended computing tasks outside of their original specification and their designers’ and programmers’ mental models. 

The Artificial Intelligence Mitigation of Emergent Execution (AIMEE) AI Exploration effort aims to address the problem of anticipating, at a system’s design stage, the models of emergent execution inherent in its design, mitigating its propensities for exploitability before they lead to actual vulnerabilities in complete, deployed systems. 

AIMEE seeks to explore whether a combination of recent advances in AI techniques such as autoencoders, evolutionary programming, deep representation learning, neural sketch learning, etc., can be used to detect, describe, and model the primitives of emergent execution directly in design-level prototypes of performance optimizations and programming abstractions rather than in complete-system implementations.