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- W2023447051 abstract "This paper proposes an innovative data-fusion/ data-mining game theoretic situation awareness and impact assessmentapproach for cyber network defense. Alerts generated by Intrusion Detection Sensors (IDSs) or Intrusion PreventionSensors (IPSs) are fed into the data refinement (Level 0) and object assessment (L1) data fusion components. High-levelsituation/threat assessment (L2/L3) data fusion based on Markov game model and Hierarchical Entity Aggregation(HEA) are proposed to refine the primitive prediction generated by adaptive feature/pattern recognition and capture newunknown features. A Markov (Stochastic) game method is used to estimate the belief of each possible cyber attackpattern. Game theory captures the nature of cyber conflicts: determination of the attacking-force strategies is tightlycoupled to determination of the defense-force strategies and vice versa. Also, Markov game theory deals with uncertaintyand incompleteness of available information. A software tool is developed to demonstrate the performance of the highlevel information fusion for cyber network defense situation and a simulation example shows the enhanced understatingof cyber-network defense." @default.
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- W2023447051 date "2007-04-09" @default.
- W2023447051 modified "2023-09-26" @default.
- W2023447051 title "A Markov game theoretic data fusion approach for cyber situational awareness" @default.
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- W2023447051 doi "https://doi.org/10.1117/12.720090" @default.
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