Acta Scientific Computer Sciences

Review Article Volume 3 Issue 10

Application of Malware Agent Based Datasets to Deep Learning Networks: A Review

Charles O Ugwunna1*, Moses O Onyesolu1 and ChukwuNonso H Nwokoye2

1Nnamdi Azikiwe University, Awka, Nigeria
1
Nigerian Correctional Service, Awka, Nigeria

*Corresponding Author: ChukwuNonso H Nwokoye, Nigerian Correctional Service, Awka, Nigeria, Awka, Nigeria.

Received: July 21, 2021; Published: September 14, 2021

Abstract

  Implanting malware into servers or endpoints in a network has become a huge possibility in institutions that use the World Wide Web to accomplish tasks. Thanks to the penetration of connectivity due to the Internet and mobile devices into personal life, work, and entertainment, as well as the increasing number of smartphones and diverse IT applications. So is the application of both machine learning (ML) and deep learning (DL) models to a variety of programs used in big data analytics and data mining. On the other hand, the agent-based model (ABM) is a computer model for simulating the activities and interactions of autonomous agents. However, we enumerated some challenges of traditional models that gave rise to the use of ABM. With the observation that both ABM and ML/DL have yet to be applied to epidemics in communication networks, we reviewed the literature surrounding the aforementioned subjects. This is to provide the appropriate background for malware spread forecasts and the development of transitional methods from ABM to DL networks during representations of epidemic theory.

Keywords: Machine Learning; Deep Learning; Malware

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Citation

Citation: Charles O Ugwunna., et al. “Application of Malware Agent Based Datasets to Deep Learning Networks: A Review". Acta Scientific Computer Sciences 3.10 (2021): 37-44.

Copyright

Copyright: © 2021 ChukwuNonso H Nwokoye., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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