Malware detection employed by visualization and deep neural network

Volume: 105, Pages: 102247 - 102247
Published: Jun 1, 2021
Abstract
With the fast growth of malware’s volume circulating in the wild, to obtain a timely and correct classification is increasingly difficult. Traditional approaches to automatic classification suffer from some limitations. The first one concerns the feature extraction: static approaches are hindered by code obfuscation techniques, while dynamic approaches are time consuming and evasion techniques often impede the correct execution of the code. The...
Paper Details
Title
Malware detection employed by visualization and deep neural network
Published Date
Jun 1, 2021
Volume
105
Pages
102247 - 102247
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