MalFCS: An effective malware classification framework with automated feature extraction based on deep convolutional neural networks-Reference-Cited by-同舟云学术

MalFCS: An effective malware classification framework with automated feature extraction based on deep convolutional neural networks

Published:2020-07 Issue: Volume:141 Page:49-58
ISSN:0743-7315
Container-title:Journal of Parallel and Distributed Computing
language:en
Short-container-title:Journal of Parallel and Distributed Computing

Author:

Xiao Guoqing,Li Jingning,Chen Yuedan,Li Kenli

Funder

National Key R&D Program of China

Programs of National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference41 articles.

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4. Structural entropy and metamorphic malware;Baysa;J. Comput. Virol. Hack. Tech.,2013

5. The malsource dataset: Quantifying complexity and code reuse in malware development;Calleja;IEEE Trans. Inf. Forensics Secur.,2019

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