Malware detection based on semi-supervised learning with malware visualization

The traditional signature-based detection method requires detailed manual analysis to extract the signatures of malicious samples, and requires a large number of manual markers to maintain the signature library, which brings a great time and resource costs, and makes it difficult to adapt to the rap...

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Autores principales:	Tan Gao, Lan Zhao, Xudong Li, Wen Chen
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	malicious sample detection collaborative learning feature fusion noise robustness Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/0c87bf4d9e3e4872be29094823d4ce70
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id	oai:doaj.org-article:0c87bf4d9e3e4872be29094823d4ce70
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spelling	oai:doaj.org-article:0c87bf4d9e3e4872be29094823d4ce702021-11-09T06:00:31ZMalware detection based on semi-supervised learning with malware visualization10.3934/mbe.20213001551-0018https://doaj.org/article/0c87bf4d9e3e4872be29094823d4ce702021-07-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021300?viewType=HTMLhttps://doaj.org/toc/1551-0018The traditional signature-based detection method requires detailed manual analysis to extract the signatures of malicious samples, and requires a large number of manual markers to maintain the signature library, which brings a great time and resource costs, and makes it difficult to adapt to the rapid generation and mutation of malware. Methods based on traditional machine learning often require a lot of time and resources in sample labeling, which results in a sufficient inventory of unlabeled samples but not directly usable. In view of these issues, this paper proposes an effective malware classification framework based on malware visualization and semi-supervised learning. This framework includes mainly three parts: malware visualization, feature extraction, and classification algorithm. Firstly, binary files are processed directly through visual methods, without assembly, decompression, and decryption; Then the global and local features of the gray image are extracted, and the visual image features extracted are fused on the whole by a special feature fusion method to eliminate the exclusion between different feature variables. Finally, an improved collaborative learning algorithm is proposed to continuously train and optimize the classifier by introducing features of inexpensive unlabeled samples. The proposed framework was evaluated over two extensively researched benchmark datasets, i.e., Malimg and Microsoft. The results show that compared with traditional machine learning algorithms, the improved collaborative learning algorithm can not only reduce the cost of sample labeling but also can continuously improve the model performance through the input of unlabeled samples, thereby achieving higher classification accuracy.Tan GaoLan ZhaoXudong Li Wen Chen AIMS Pressarticlemalicious sample detectioncollaborative learningfeature fusionnoise robustnessBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 5995-6011 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	malicious sample detection collaborative learning feature fusion noise robustness Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	malicious sample detection collaborative learning feature fusion noise robustness Biotechnology TP248.13-248.65 Mathematics QA1-939 Tan Gao Lan Zhao Xudong Li Wen Chen Malware detection based on semi-supervised learning with malware visualization
description	The traditional signature-based detection method requires detailed manual analysis to extract the signatures of malicious samples, and requires a large number of manual markers to maintain the signature library, which brings a great time and resource costs, and makes it difficult to adapt to the rapid generation and mutation of malware. Methods based on traditional machine learning often require a lot of time and resources in sample labeling, which results in a sufficient inventory of unlabeled samples but not directly usable. In view of these issues, this paper proposes an effective malware classification framework based on malware visualization and semi-supervised learning. This framework includes mainly three parts: malware visualization, feature extraction, and classification algorithm. Firstly, binary files are processed directly through visual methods, without assembly, decompression, and decryption; Then the global and local features of the gray image are extracted, and the visual image features extracted are fused on the whole by a special feature fusion method to eliminate the exclusion between different feature variables. Finally, an improved collaborative learning algorithm is proposed to continuously train and optimize the classifier by introducing features of inexpensive unlabeled samples. The proposed framework was evaluated over two extensively researched benchmark datasets, i.e., Malimg and Microsoft. The results show that compared with traditional machine learning algorithms, the improved collaborative learning algorithm can not only reduce the cost of sample labeling but also can continuously improve the model performance through the input of unlabeled samples, thereby achieving higher classification accuracy.
format	article
author	Tan Gao Lan Zhao Xudong Li Wen Chen
author_facet	Tan Gao Lan Zhao Xudong Li Wen Chen
author_sort	Tan Gao
title	Malware detection based on semi-supervised learning with malware visualization
title_short	Malware detection based on semi-supervised learning with malware visualization
title_full	Malware detection based on semi-supervised learning with malware visualization
title_fullStr	Malware detection based on semi-supervised learning with malware visualization
title_full_unstemmed	Malware detection based on semi-supervised learning with malware visualization
title_sort	malware detection based on semi-supervised learning with malware visualization
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/0c87bf4d9e3e4872be29094823d4ce70
work_keys_str_mv	AT tangao malwaredetectionbasedonsemisupervisedlearningwithmalwarevisualization AT lanzhao malwaredetectionbasedonsemisupervisedlearningwithmalwarevisualization AT xudongli malwaredetectionbasedonsemisupervisedlearningwithmalwarevisualization AT wenchen malwaredetectionbasedonsemisupervisedlearningwithmalwarevisualization
_version_	1718441266875727872

Malware detection based on semi-supervised learning with malware visualization

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