KEYWORDS: Clouds, Information security, Systems modeling, Information fusion, Sensing systems, Mathematical modeling, Fuzzy logic, Data modeling, Data fusion, Sensors
Layered sensing systems involve operation of several layers of sensing with different capabilities integrated into one
whole system. The integrated layers of sensing must share information and local decisions across layers for better
situation awareness. This research focused on the development of a model for decision making and fusion at the
information level in layered sensing systems using the cloud model for uncertainty processing. In this research, the
addition of a new processing level to the Joint Directors of Laboratories (JDL) processing model is proposed. The new
processing level is called "Information Assessment, Fusion, and Control (IAFC)". Through this level, the different
layers of a layered sensing system evaluate information about a given situation in terms of threat level and make a
decision. The information assessment and control processing module were able to assess the threat level of a situation
accurately and exchange assessments in order to determine the overall situation's threat level among all layers. The
uncertain decisions were fused together to a unified decision using the cloud model of uncertainty processing
methodology. Using this methodology, a cognitive element was added to the process of information assessment module
leading to more accurate situation awareness.
KEYWORDS: Clouds, Information security, Data modeling, Probability theory, Mathematical modeling, Fuzzy logic, 3D modeling, Sensors, Data processing, Computer security
Uncertainty plays decisive role in the confidence of the decisions made about events. For example, in situation awareness,
decision-making is faced with two types of uncertainties; information uncertainty and data uncertainty. Data uncertainty
exists due to noise in sensor measurements and is classified as randomness. Information uncertainty is due to ambiguity of
using (words) to describe events. This uncertainty is known as fuzziness. Typically, these two types of uncertainties are
handled separately using two different theories. Randomness is modeled by probability theory, while fuzzy-logic is used to
address fuzziness. In this paper we used the Cloud computation theory to treat data randomness and information fuzziness in
one single model. First, we described the Cloud theory then used the theory to generate one and two-dimensional Cloud
models. Second, we used the Cloud models to capture and process data randomness and fuzziness in information relative to
decision-making in situation awareness. Finally, we applied the models to generate security decisions for security monitoring
of sensitive area. Testing results are reported at the end of the paper.
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