Deep visualization classification method for malicious code based on Ngram-TFIDF
With the continuous increase in the scale and variety of malware, traditional malware analysis methods, which relied on manual feature extraction, become time-consuming and error-prone, rendering them unsuitable. To improve detection efficiency and accuracy, a deep visualization classification metho...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024115/ |
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| _version_ | 1841539241940615168 |
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| author | WANG Jinwei CHEN Zhengjia XIE Xue LUO Xiangyang MA Bin |
| author_facet | WANG Jinwei CHEN Zhengjia XIE Xue LUO Xiangyang MA Bin |
| author_sort | WANG Jinwei |
| collection | DOAJ |
| description | With the continuous increase in the scale and variety of malware, traditional malware analysis methods, which relied on manual feature extraction, become time-consuming and error-prone, rendering them unsuitable. To improve detection efficiency and accuracy, a deep visualization classification method for malicious code based on Ngram-TFIDF was proposed. The malware dataset was processed by combining N-gram and TF-IDF techniques, transforming it into grayscale images. Subsequently, the CBAM was introduced and the number of dense blocks was adjusted to construct the DenseNet88_CBAM network model for grayscale image classification. Experimental results demonstrate that the proposed method achieves superior classification performance, with accuracy improvements of 1.11% and 9.28% in malware family classification and type classification, respectively. |
| format | Article |
| id | doaj-art-eda2174f1c3c4dc89a2444e6014aae9e |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-eda2174f1c3c4dc89a2444e6014aae9e2025-01-14T07:24:32ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-06-014516017563977266Deep visualization classification method for malicious code based on Ngram-TFIDFWANG JinweiCHEN ZhengjiaXIE XueLUO XiangyangMA BinWith the continuous increase in the scale and variety of malware, traditional malware analysis methods, which relied on manual feature extraction, become time-consuming and error-prone, rendering them unsuitable. To improve detection efficiency and accuracy, a deep visualization classification method for malicious code based on Ngram-TFIDF was proposed. The malware dataset was processed by combining N-gram and TF-IDF techniques, transforming it into grayscale images. Subsequently, the CBAM was introduced and the number of dense blocks was adjusted to construct the DenseNet88_CBAM network model for grayscale image classification. Experimental results demonstrate that the proposed method achieves superior classification performance, with accuracy improvements of 1.11% and 9.28% in malware family classification and type classification, respectively.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024115/deep learningdata visualizationmalicious code detection and classification |
| spellingShingle | WANG Jinwei CHEN Zhengjia XIE Xue LUO Xiangyang MA Bin Deep visualization classification method for malicious code based on Ngram-TFIDF Tongxin xuebao deep learning data visualization malicious code detection and classification |
| title | Deep visualization classification method for malicious code based on Ngram-TFIDF |
| title_full | Deep visualization classification method for malicious code based on Ngram-TFIDF |
| title_fullStr | Deep visualization classification method for malicious code based on Ngram-TFIDF |
| title_full_unstemmed | Deep visualization classification method for malicious code based on Ngram-TFIDF |
| title_short | Deep visualization classification method for malicious code based on Ngram-TFIDF |
| title_sort | deep visualization classification method for malicious code based on ngram tfidf |
| topic | deep learning data visualization malicious code detection and classification |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024115/ |
| work_keys_str_mv | AT wangjinwei deepvisualizationclassificationmethodformaliciouscodebasedonngramtfidf AT chenzhengjia deepvisualizationclassificationmethodformaliciouscodebasedonngramtfidf AT xiexue deepvisualizationclassificationmethodformaliciouscodebasedonngramtfidf AT luoxiangyang deepvisualizationclassificationmethodformaliciouscodebasedonngramtfidf AT mabin deepvisualizationclassificationmethodformaliciouscodebasedonngramtfidf |