Method of anti-confusion texture feature descriptor for malware images
It is a new method that uses image processing and machine learning algorithms to classify malware samples in malware visualization field.The texture feature description method has great influence on the result.To solve this problem,a new method was presented that joints global feature of GIST with l...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2018-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018227/ |
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| _version_ | 1841539434404642816 |
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| author | Yashu LIU Zhihai WANG Hanbing YAN Yueran HOU Yukun LAI |
| author_facet | Yashu LIU Zhihai WANG Hanbing YAN Yueran HOU Yukun LAI |
| author_sort | Yashu LIU |
| collection | DOAJ |
| description | It is a new method that uses image processing and machine learning algorithms to classify malware samples in malware visualization field.The texture feature description method has great influence on the result.To solve this problem,a new method was presented that joints global feature of GIST with local features of LBP or dense SIFT in order to construct combinative descriptors of malware gray-scale images.Using those descriptors,the malware classification performance was greatly improved in contrast to traditional method,especially for those samples have higher similarity in the different families,or those have lower similarity in the same family.A lot of experiments show that new method is much more effective and general than traditional method.On the confusing dataset,the accuracy rate of classification has been greatly improved. |
| format | Article |
| id | doaj-art-7dab675e4b664145979657c292aead72 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2018-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-7dab675e4b664145979657c292aead722025-01-14T07:15:41ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2018-11-0139445359721542Method of anti-confusion texture feature descriptor for malware imagesYashu LIUZhihai WANGHanbing YANYueran HOUYukun LAIIt is a new method that uses image processing and machine learning algorithms to classify malware samples in malware visualization field.The texture feature description method has great influence on the result.To solve this problem,a new method was presented that joints global feature of GIST with local features of LBP or dense SIFT in order to construct combinative descriptors of malware gray-scale images.Using those descriptors,the malware classification performance was greatly improved in contrast to traditional method,especially for those samples have higher similarity in the different families,or those have lower similarity in the same family.A lot of experiments show that new method is much more effective and general than traditional method.On the confusing dataset,the accuracy rate of classification has been greatly improved.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018227/malware visualizationimage texturefeature descriptorsmalware classification |
| spellingShingle | Yashu LIU Zhihai WANG Hanbing YAN Yueran HOU Yukun LAI Method of anti-confusion texture feature descriptor for malware images Tongxin xuebao malware visualization image texture feature descriptors malware classification |
| title | Method of anti-confusion texture feature descriptor for malware images |
| title_full | Method of anti-confusion texture feature descriptor for malware images |
| title_fullStr | Method of anti-confusion texture feature descriptor for malware images |
| title_full_unstemmed | Method of anti-confusion texture feature descriptor for malware images |
| title_short | Method of anti-confusion texture feature descriptor for malware images |
| title_sort | method of anti confusion texture feature descriptor for malware images |
| topic | malware visualization image texture feature descriptors malware classification |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018227/ |
| work_keys_str_mv | AT yashuliu methodofanticonfusiontexturefeaturedescriptorformalwareimages AT zhihaiwang methodofanticonfusiontexturefeaturedescriptorformalwareimages AT hanbingyan methodofanticonfusiontexturefeaturedescriptorformalwareimages AT yueranhou methodofanticonfusiontexturefeaturedescriptorformalwareimages AT yukunlai methodofanticonfusiontexturefeaturedescriptorformalwareimages |