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• W4360993414 abstract "Nowadays, the problems confronted besides cyber security manufacturers of between fade tens of billions to across country and world. Cyber Supply Chain (CSC) system is complex which involves different sub-systems performing various tasks. Safeguards such as distribution network has been tricky due to inherent risks and attacks from just about any system can be controlled which can still be abused at every juncture within in the distribution chain. This could trigger a massive interruption upon that business operations connectivity. Therefore, it was indeed supreme useful for understanding but also criterion it and attacks such that agency could really undergo master station initiatives is for operational risk management. To demonstrate the applicability of our approach, malware detection dataset is gathered and a number of ML algorithms, i.e., XGBoost, Gradient Boosting, are used to develop predictive analytics using the Kaggle Malware detection dataset." @default.
• W4360993414 created "2023-03-30" @default.
• W4360993414 creator A5002332103 @default.
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• W4360993414 date "2023-01-01" @default.
• W4360993414 modified "2023-09-24" @default.
• W4360993414 title "Ensemble Based Cyber Threat Analysis for Supply Chain Management" @default.
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• W4360993414 doi "https://doi.org/10.1007/978-3-031-27499-2_45" @default.
• W4360993414 hasPublicationYear "2023" @default.
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