Communication Analysis of Network‐Centric Warfare via Transformation of System of Systems Model into Integrated System Model Using Neural Network

Kang, Bo Sun; Seo, Kyung-Min; Kim, Tag Gon

doi:10.1155/2018/6201356

Cited by 10 publications

(17 citation statements)

References 35 publications

(32 reference statements)

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“…In the past, the integration of conceptual models and simulation models has been paid closer attention. For example, researchers have developed architectural models enabled executable simulation models based on colored Petri net [42,43], agent-based modeling [44], and network-based simulation [45,46]. However, not much work has focused on building the connection between DoDAF models and optimization-based models or analytical models [10].…”

Section: Mbse and Architectural Models Enabled Analysismentioning

confidence: 99%

Architectural Models Enabled Dynamic Optimization for System-of-Systems Evolution

2020

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System of Systems (SoS) is designed to deliver value to participant stakeholders in a dynamic and uncertain environment where new systems are added and current systems are removed continuously and on their own volition. is requires effective evolution management at the SoS architectural level with adequate support of process, methods, and tools. is paper follows the principle of Model-Based Systems Engineering (MBSE) and develops a holistic framework integrating MBSE conceptual representations and approximate dynamic programming (ADP) to support the SoS evolution. e conceptual models provide a common architectural representation to improve communication between various decision makers while the dynamic optimization method suggests evolution planning decisions from the analytical perspective. e Department of Defense Architecture Framework (DoDAF) models using Systems Modeling Language (SysML) are used as MBSE artifacts to connect with ADP modeling elements through DoDAF metamodels to increase information traceability and reduce unnecessary information loss. Using a surface warfare SoS as an example, this paper demonstrates and explains the procedures of developing DoDAF models, mapping DoDAF models to ADP elements, formulating ADP formulation, and generating evolutionary decisions. e effectiveness of using ADP in supporting evolution to achieve a near-optimal solution that can maximize the SoS capability over time is illustrated by comparing ADP solution to other alternative solutions. e entire framework also sheds light on bridging the DoDAF-based conceptual models and other mathematical optimization methods.

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Section: Mbse and Architectural Models Enabled Analysismentioning

confidence: 99%

Architectural Models Enabled Dynamic Optimization for System-of-Systems Evolution

2020

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“…Although the applicable case of each method is different, the efficiency of the optimization can be further increased by reducing k and h simultaneously if both methods can be used together. Therefore, intDepT = inter-departure time of the C requests PDR = packet-delivery ratio DELAY = end-to-end delay intDept intDept Figure 6: Transformation description via the model hypothesis and variable estimation [27]. 6 Complexity in this section, we introduce a GA with R&S to improve the efficiency of the optimization for the SoS-based NCW stochastic simulation model.…”

Section: Optimization Processmentioning

confidence: 99%

“…However, this process also requires the considerable time because it takes a long time per run of the SoS-based simulation model. On the other hand, another approach to dealing with the long execution time per run is to transform the SoS-based simulation model into an integrated simulation model via the abstraction of the model [25][26][27]. Although this can reduce the time required to conduct the exploratory analysis, for example, a full search on the design space, the "what-if" analysis is also time-consuming because the integrated simulation model requires somewhat execution time [28,29].…”

Section: Introductionmentioning

confidence: 99%

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Simulation‐Based Optimization on the System‐of‐Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network‐Centric Warfare

et al. 2018

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Simulation of a system-of-systems (SoS) model, which consists of a combat model and a network model, has been used to analyze the performance of network-centric warfare in detail. However, finding the combat model parameters satisfying the required combat power using simulation can take a long time for two reasons: (1) the prolonged execution time per simulation run and (2) the enormous number of simulation runs. This paper proposes a simulation-based optimization method for the SoS-based simulation model to overcome these problems. The method consists of two processes: (1) the transformation of the SoS-based model into an integrated model using the neural network to reduce the execution time and (2) the optimization of the integrated model using the genetic algorithm with ranking and selection to decrease the number of simulation runs. The experimental result reveals that the proposed method significantly reduced the time for finding the optimal combat parameters with an acceptable level of accuracy.

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“…The most ideal network security defense system can defend against all attacks in the network. However, this "at all costs" defense idea can not be applicable to the realistic network security defense [2]. The essence of network attack and defense can be formulated as a game between attacker and defender, its main process is to make reasonable decisions under limited resources, which is accordance with the game theory [3].Therefore, the network security defense method based on game theory has gradually become a hot topic.…”

Section: Introductionmentioning

confidence: 99%

Optimal Network Defense Strategy Selection Based on Markov Bayesian Game

2019

KSII TIIS

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The existing defense strategy selection methods based on game theory basically select the optimal defense strategy in the form of mixed strategy. However, it is hard for network managers to understand and implement the defense strategy in this way. To address this problem, we constructed the incomplete information stochastic game model for the dynamic analysis to predict multi-stage attack-defense process by combining Bayesian game theory and the Markov decision-making method. In addition, the payoffs are quantified from the impact value of attack-defense actions. Based on previous statements, we designed an optimal defense strategy selection method. The optimal defense strategy is selected, which regards defense effectiveness as the criterion. The proposed method is feasibly verified via a representative experiment. Compared to the classical strategy selection methods based on the game theory, the proposed method can select the optimal strategy of the multi-stage attack-defense process in the form of pure strategy, which has been proved more operable than the compared ones.

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Communication Analysis of Network‐Centric Warfare via Transformation of System of Systems Model into Integrated System Model Using Neural Network

Cited by 10 publications

References 35 publications

Architectural Models Enabled Dynamic Optimization for System-of-Systems Evolution

Architectural Models Enabled Dynamic Optimization for System-of-Systems Evolution

Simulation‐Based Optimization on the System‐of‐Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network‐Centric Warfare

Optimal Network Defense Strategy Selection Based on Markov Bayesian Game

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