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Analytic-agent cyber dynamical systems analysis and design method for modeling spatio-temporal factors of malware propagation in wireless sensor networks

Modelers often apply analytical (differential equation-based) epidemic models that mostly characterize the behavior of the network compartments with passage of time. Beyond temporal characterization, agent modeling promises the achievement of relevant spatial (stochastic and heterogeneous) represent...

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Detalles Bibliográficos
Autores principales: Nwokoye, ChukwuNonso, Umeh, Ikechukwu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223030/
https://www.ncbi.nlm.nih.gov/pubmed/30425935
http://dx.doi.org/10.1016/j.mex.2018.10.005
Descripción
Sumario:Modelers often apply analytical (differential equation-based) epidemic models that mostly characterize the behavior of the network compartments with passage of time. Beyond temporal characterization, agent modeling promises the achievement of relevant spatial (stochastic and heterogeneous) representations. Arising from the combination of the prevalent analytical and agent methods (gleaned from extant literature) is a new method called the Analytic-Agent Cyber Dynamical Systems Analysis and Design Method (A(2)CDSADM); a modification of the Agent Oriented Analysis and Design (AOAD). Using hypothetical wireless sensor network (WSN) cases, A(2)CDSADM alleviates the lack of field data/lack of real geographical locations of the occurrence of particular cases by creating an analytical benchmark model for initial validation of the resulting agent model and ensures its easy modifiability and reproducibility. More so, it helps achieve the complementary/generative contribution of agent modeling, diminishes the less-tractable nature of representing/analyzing WSN spatial features and provides a formalized method for performing comparative epidemic studies. Also, A(2)CDSADM covers the additional features for: • Generating the (analytical) equilibriums of WSN. • Performing continuous validation (at several points) in order to ensure model accuracy/suitability for real-world decision making. • Creating a high level conceptual model containing the envisaged WSN features to be represented.