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Baraa Tareq Hammad on Muck Rack

Baraa Tareq Hammad

As seen in: MDPI
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Articles

Employing Different Algorithms of Lightweight Convolutional Neural Network Models in Image Distortion Classification

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By Ismail Ahmed, Falah Amer Abdulazeez, Baraa Tareq Hammad, Ismail Taha
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Examining the Performance of Various Pretrained Convolutional Neural Network Models in Malware Detection

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By Ismail Ahmed, Baraa Tareq Hammad, Falah Amer Abdulazeez, Falah Amer
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Binary and Multi-Class Malware Threads Classification

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By Ismail Ahmed, Norziana Jamil, Baraa Tareq Hammad, Marina Md. Din
| MDPI
Abstract : The security of a computer system can be harmed by specific applications, such as malware. Malware comprises unwanted, dangerous enemies that aim to compromise the security and generate significant loss. Consequently, Malware Detection (MD) and Malware Classification (MC) has emerged as a key issue for the cybersecurity society. MD only involves locating malware without determining what kind of malware it is, but MC comprises assigning a class of malware to a particular sample.
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