Current cyber-related security and safety risks are unprecedented, due in no small part to information overload
and skilled cyber-analyst shortages. Advances in decision support and Situation Awareness (SA) tools are
required to support analysts in risk mitigation. Inspired by human intelligence, research in Artificial Intelligence
(AI) and Computational Intelligence (CI) have provided successful engineering solutions in complex domains
including cyber. Current AI approaches aggregate large volumes of data to infer the general from the particular,
i.e. inductive reasoning (pattern-matching) and generally cannot infer answers not previously programmed.
Whereas humans, rarely able to reason over large volumes of data, have successfully reached the top of the
food chain by inferring situations from partial or even partially incorrect information, i.e. abductive reasoning
(pattern-completion); generating a hypothetical explanation of observations. In order to achieve an engineering
advantage in computational decision support and SA we leverage recent research in human consciousness, the role
consciousness plays in decision making, modeling the units of subjective experience which generate consciousness,
qualia. This paper introduces a novel computational implementation of a Cognitive Modeling Architecture (CMA)
which incorporates concepts of consciousness. We apply our model to the malware type-classification task. The
underlying methodology and theories are generalizable to many domains.
KEYWORDS: Process modeling, Document management, Situational awareness sensors, Information technology, Data processing, Statistical analysis, Information security, Cognitive modeling, Data modeling, Network security
Military organizations embed information systems and networking technologies into their core mission processes as a
means to increase operational efficiency, improve decision making quality, and shorten the "kill chain". Unfortunately,
this dependence can place the mission at risk when the loss or degradation of the confidentiality, integrity, availability,
non-repudiation, or authenticity of a critical information resource or flow occurs. Since the accuracy, conciseness, and
timeliness of the information used in command decision making processes impacts the quality of these decisions, and
hence, the operational mission outcome; it is imperative to explicitly recognize, quantify, and document critical missioninformation
dependencies in order to gain a true appreciation of operational risk. We conjecture what is needed is a
structured process to provide decision makers with real-time awareness of the status of critical information resources and
timely notification of estimated mission impact, from the time an information incident is declared, until the incident is
fully remediated. In this paper, we discuss our initial research towards the development of a mission impact estimation
engine which fuses information from subject matter experts, historical mission impacts, and explicit mission models to
provide the ability to estimate the mission impacts resulting from an information incident in real-time.
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