TY - JOUR
T1 - Static versus dynamic data information fusion analysis using DDDAS for cyber security trust
AU - Blasch, Erik
AU - Al-Nashif, Youssif
AU - Hariri, Salim
N1 - Funding Information: This work is partially supported by AFOSR DDDAS award number FA95550-12-1-0241, and National Science Foundation research projects NSF IIP-0758579, NCS-0855087 and IIP-1127873.
PY - 2014
Y1 - 2014
N2 - Information fusion includes signals, features, and decision-level analysis over various types of data including imagery, text, and cyber security detection. With the maturity of data processing, the explosion of big data, and the need for user acceptance; the Dynamic Data-Driven Application System (DDDAS) philosophy fosters insights into the usability of information systems solutions. In this paper, we exp lore a notion of an adaptive adjustment of secure communication trust analysis that seeks a balance between standard static solutions versus dynamic -data driven updates. A use case is provided in determining trust for a cyber security scenario exploring comparisons of Bayesian versus evidential reasoning for dynamic security detection updates. Using the evidential reasoning proportional conflict redistribution (PCR) method, we demonstrate improved trust for dynamically changing detections of denial of service attacks.
AB - Information fusion includes signals, features, and decision-level analysis over various types of data including imagery, text, and cyber security detection. With the maturity of data processing, the explosion of big data, and the need for user acceptance; the Dynamic Data-Driven Application System (DDDAS) philosophy fosters insights into the usability of information systems solutions. In this paper, we exp lore a notion of an adaptive adjustment of secure communication trust analysis that seeks a balance between standard static solutions versus dynamic -data driven updates. A use case is provided in determining trust for a cyber security scenario exploring comparisons of Bayesian versus evidential reasoning for dynamic security detection updates. Using the evidential reasoning proportional conflict redistribution (PCR) method, we demonstrate improved trust for dynamically changing detections of denial of service attacks.
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U2 - 10.1016/j.procs.2014.05.117
DO - 10.1016/j.procs.2014.05.117
M3 - Conference article
SN - 1877-0509
VL - 29
SP - 1299
EP - 1313
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 14th Annual International Conference on Computational Science, ICCS 2014
Y2 - 10 June 2014 through 12 June 2014
ER -