Research on lightweight malware classification method based on image domain
To address the high deployment costs and long prediction times associated with traditional malware classification methods, a lightweight malware visualization classification method was proposed. Firstly, a CBG algorithm was introduced to solve the problems of imbalanced image sizes and excessive noi...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2025-03-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025035 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850154292757921792 |
|---|---|
| author | SUN Jingzhang CHENG Yinan ZOU Binghui QIAO Tonghua FU Sizheng ZHANG Qi CAO Chunjie |
| author_facet | SUN Jingzhang CHENG Yinan ZOU Binghui QIAO Tonghua FU Sizheng ZHANG Qi CAO Chunjie |
| author_sort | SUN Jingzhang |
| collection | DOAJ |
| description | To address the high deployment costs and long prediction times associated with traditional malware classification methods, a lightweight malware visualization classification method was proposed. Firstly, a CBG algorithm was introduced to solve the problems of imbalanced image sizes and excessive noise in malware images. Then, to capture feature relationships effectively and reduce computational complexity, a lightweight channel attention mechanism was implemented. This mechanism guided the model to focus on more informative features, while depthwise separable convolution further reduced the number of model parameters. Experimental results on three large malware datasets, MalImg, BIG2015, and BODMAS, demonstrate that the proposed model achieved classification accuracies of 99.68%, 99.45%, and 93.12%, with model sizes of 442 KB, 414 KB, and 423 KB, and prediction times of 14.12 ms, 11.09 ms, and 4.11 ms per image, respectively. This method demonstrates state-of-the-art performance in accuracy, model size, and inference speed. |
| format | Article |
| id | doaj-art-3d672664bef341bbad571f93ea1869dc |
| institution | OA Journals |
| issn | 1000-436X |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-3d672664bef341bbad571f93ea1869dc2025-08-20T02:25:24ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2025-03-014618719888697883Research on lightweight malware classification method based on image domainSUN JingzhangCHENG YinanZOU BinghuiQIAO TonghuaFU SizhengZHANG QiCAO ChunjieTo address the high deployment costs and long prediction times associated with traditional malware classification methods, a lightweight malware visualization classification method was proposed. Firstly, a CBG algorithm was introduced to solve the problems of imbalanced image sizes and excessive noise in malware images. Then, to capture feature relationships effectively and reduce computational complexity, a lightweight channel attention mechanism was implemented. This mechanism guided the model to focus on more informative features, while depthwise separable convolution further reduced the number of model parameters. Experimental results on three large malware datasets, MalImg, BIG2015, and BODMAS, demonstrate that the proposed model achieved classification accuracies of 99.68%, 99.45%, and 93.12%, with model sizes of 442 KB, 414 KB, and 423 KB, and prediction times of 14.12 ms, 11.09 ms, and 4.11 ms per image, respectively. This method demonstrates state-of-the-art performance in accuracy, model size, and inference speed.http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025035malware classificationimage enhancementlightweight modellightweight channel attention |
| spellingShingle | SUN Jingzhang CHENG Yinan ZOU Binghui QIAO Tonghua FU Sizheng ZHANG Qi CAO Chunjie Research on lightweight malware classification method based on image domain Tongxin xuebao malware classification image enhancement lightweight model lightweight channel attention |
| title | Research on lightweight malware classification method based on image domain |
| title_full | Research on lightweight malware classification method based on image domain |
| title_fullStr | Research on lightweight malware classification method based on image domain |
| title_full_unstemmed | Research on lightweight malware classification method based on image domain |
| title_short | Research on lightweight malware classification method based on image domain |
| title_sort | research on lightweight malware classification method based on image domain |
| topic | malware classification image enhancement lightweight model lightweight channel attention |
| url | http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.2025035 |
| work_keys_str_mv | AT sunjingzhang researchonlightweightmalwareclassificationmethodbasedonimagedomain AT chengyinan researchonlightweightmalwareclassificationmethodbasedonimagedomain AT zoubinghui researchonlightweightmalwareclassificationmethodbasedonimagedomain AT qiaotonghua researchonlightweightmalwareclassificationmethodbasedonimagedomain AT fusizheng researchonlightweightmalwareclassificationmethodbasedonimagedomain AT zhangqi researchonlightweightmalwareclassificationmethodbasedonimagedomain AT caochunjie researchonlightweightmalwareclassificationmethodbasedonimagedomain |