Authors:
John Carbone, Forcepoint LLC
Dr. James Crowder, Colorado Engineering Inc.
Abstract:
Historically, research shows analysis, characterization, and classification of complex heterogeneous non-linear systems and interactions have been difficult to accurately understand and effectively model. Synonymous, exponential growth of Internet of Things (IoT), Cyber Physical Systems, and the litter of current accidental and unscrupulous cyber events portray an ever-challenging security environment wrought with complexity, ambiguity, and non-linearity. Thus, providing significant incentive to industry and academia towards advanced, predictive solutions to reduce persistent global threats. Recent advances in Artificial Intelligence (AI) and Information Theoretic Methods (ITM) are benefitting disciplines struggling with learning from rapidly increasing data volume, velocity, and complexity. Research shows Axiomatic Design (AD) providing design and datum disambiguation for complex systems utilizing information content reduction. Therefore, we propose a comprehensive transdisciplinary AD, AI/ML, ITM, approach combining axiomatic design with advanced, novel, and adaptive machine-based learning techniques. We show how to significantly reduce risks and complexity by improving cyber system adaptiveness, enhancing cyber system learning, and increasing cyber system prediction and insight potential where today context is sorely lacking. We provide an approach for deeper contextual understanding of disjointed cyber events by improving knowledge density (KD) (how much we know about a given event) and knowledge fidelity (KF) (how well do we know) ultimately improving decision mitigation quality and autonomy. We improve classification and understanding of cyber data, reduce system non-linearity and cyber threat risk, thereby, increasing efficiency by reducing labor and system costs, and “peace of mind.”
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