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 |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1000-436X |