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: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
AIMS Press
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/0c87bf4d9e3e4872be29094823d4ce70 |
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| Sumario: | 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. |
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