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A Malicious Code Detection Method Based on FF-MICNN in the Internet of Things

It is critical to detect malicious code for the security of the Internet of Things (IoT). Therefore, this work proposes a malicious code detection algorithm based on the novel feature fusion–malware image convolutional neural network (FF-MICNN). This method combines a feature fusion algorithm with d...

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Detalles Bibliográficos
Autores principales: Zhang, Wenbo, Feng, Yongxin, Han, Guangjie, Zhu, Hongbo, Tan, Xiaobo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695768/
https://www.ncbi.nlm.nih.gov/pubmed/36433343
http://dx.doi.org/10.3390/s22228739
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author Zhang, Wenbo
Feng, Yongxin
Han, Guangjie
Zhu, Hongbo
Tan, Xiaobo
author_facet Zhang, Wenbo
Feng, Yongxin
Han, Guangjie
Zhu, Hongbo
Tan, Xiaobo
author_sort Zhang, Wenbo
collection PubMed
description It is critical to detect malicious code for the security of the Internet of Things (IoT). Therefore, this work proposes a malicious code detection algorithm based on the novel feature fusion–malware image convolutional neural network (FF-MICNN). This method combines a feature fusion algorithm with deep learning. First, the malicious code is transformed into grayscale image features by image technology, after which the opcode sequence features of the malicious code are extracted by the n-gram technique, and the global and local features are fused by feature fusion technology. The fused features are input into FF-MICNN for training, and an appropriate classifier is selected for detection. The results of experiments show that the proposed algorithm exhibits improvements in its detection speed, the comprehensiveness of features, and accuracy as compared with other algorithms. The accuracy rate of the proposed algorithm is also 0.2% better than that of a detection algorithm based on a single feature.
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spelling pubmed-96957682022-11-26 A Malicious Code Detection Method Based on FF-MICNN in the Internet of Things Zhang, Wenbo Feng, Yongxin Han, Guangjie Zhu, Hongbo Tan, Xiaobo Sensors (Basel) Article It is critical to detect malicious code for the security of the Internet of Things (IoT). Therefore, this work proposes a malicious code detection algorithm based on the novel feature fusion–malware image convolutional neural network (FF-MICNN). This method combines a feature fusion algorithm with deep learning. First, the malicious code is transformed into grayscale image features by image technology, after which the opcode sequence features of the malicious code are extracted by the n-gram technique, and the global and local features are fused by feature fusion technology. The fused features are input into FF-MICNN for training, and an appropriate classifier is selected for detection. The results of experiments show that the proposed algorithm exhibits improvements in its detection speed, the comprehensiveness of features, and accuracy as compared with other algorithms. The accuracy rate of the proposed algorithm is also 0.2% better than that of a detection algorithm based on a single feature. MDPI 2022-11-12 /pmc/articles/PMC9695768/ /pubmed/36433343 http://dx.doi.org/10.3390/s22228739 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Wenbo
Feng, Yongxin
Han, Guangjie
Zhu, Hongbo
Tan, Xiaobo
A Malicious Code Detection Method Based on FF-MICNN in the Internet of Things
title A Malicious Code Detection Method Based on FF-MICNN in the Internet of Things
title_full A Malicious Code Detection Method Based on FF-MICNN in the Internet of Things
title_fullStr A Malicious Code Detection Method Based on FF-MICNN in the Internet of Things
title_full_unstemmed A Malicious Code Detection Method Based on FF-MICNN in the Internet of Things
title_short A Malicious Code Detection Method Based on FF-MICNN in the Internet of Things
title_sort malicious code detection method based on ff-micnn in the internet of things
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695768/
https://www.ncbi.nlm.nih.gov/pubmed/36433343
http://dx.doi.org/10.3390/s22228739
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