Volume 29 Issue 6
Dec.  2020
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WANG Lele, WANG Binqiang, ZHAO Peipei, LIU Ruyi, LIU Jiangang, MIAO Qiguang. Malware Detection Algorithm Based on the Attention Mechanism and ResNet[J]. Chinese Journal of Electronics, 2020, 29(6): 1054-1060. doi: 10.1049/cje.2020.09.006
Citation: WANG Lele, WANG Binqiang, ZHAO Peipei, LIU Ruyi, LIU Jiangang, MIAO Qiguang. Malware Detection Algorithm Based on the Attention Mechanism and ResNet[J]. Chinese Journal of Electronics, 2020, 29(6): 1054-1060. doi: 10.1049/cje.2020.09.006

Malware Detection Algorithm Based on the Attention Mechanism and ResNet

doi: 10.1049/cje.2020.09.006
Funds:  This work is supported by the National Natural Science Foundation of China (No.61902296), Xi'an Key Laboratory of Big Data and Intelligent Vision (No.201805053ZDCG37), the Fundamental Research Funds for the Central Universities (No.JBF180301, No.XJS190307), the National Natural Science Foundations of Shaanxi Province (No.2020JQ-330, No.2020JM-195), and the National Science Foundation for Post-doctoral Scientists of China (No.2019M663640).
More Information
  • Corresponding author: ZHAO Peipei (corresponding author) received the M.E. degree from the School of Computer Science and Technology, Xidian University in 2016. She is currently working toward the Ph.D. degree at Xidian University. Her research interests include fine-grained visual categorization, pattern recognition and deep learning. (Email:zhpp8826210@163.com)
  • Received Date: 2019-10-30
  • Publish Date: 2020-12-25
  • The cost of misclassifying a malware program as normal is often higher than that of misclassifying a normal program as malware. Therefore, how to improve the detection accuracy of malware programs is a very important problem. This paper proposes a deep learning malware program detection algorithm based on attention mechanism. Word2Vec model is used to map the Application programming interface (API) into word vectors, and all word vectors of each sample are arranged into a matrix with the same size. On this basis, residual network is used to extract features of samples. The features are input into the attention mechanism to learn the similarity between samples. Then, the features are weighted with the similarity to obtain the new features with better robustness. The new features and the original features are added element by element to obtain the sample features more suitable for classification. Finally, samples are classified by classifier. Experiments show that the classification effect of the proposed method is better than that of the traditional machine learning method.
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