Quantitative Analysis of Worm Transmission and Insider Risks in Air-Gapped Networking Using a Novel Machine Learning Approach

Quantitative Analysis of Worm Transmission and Insider Risks in Air-Gapped Networking Using a Novel Machine Learning Approach

Researchers and practitioners in the fields of science and engineering encounter significant challenges when it comes to mitigating the proliferation of computer worms, owing to their rapid spread within computer and communication networks. This study delves into a comprehensive analysis of the math...

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Bibliographic Details
Main Authors: Muhammad Sulaiman, Awais Khan, Addisu Negash Ali, Ghaylen Laouini, Fahad Sameer Alshammari
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
SEIQV model
insider threat
artificial neural networks
machine learning
system of differential equations
surrogate solutions
Online Access:https://ieeexplore.ieee.org/document/10274075/

Internet

https://ieeexplore.ieee.org/document/10274075/

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