Volume 29 Issue 6
Dec.  2020
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WANG Lele, WANG Binqiang, ZHAO Peipei, et al., “Malware Detection Algorithm Based on the Attention Mechanism and ResNet,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1054-1060, 2020, doi: 10.1049/cje.2020.09.006
Citation: WANG Lele, WANG Binqiang, ZHAO Peipei, et al., “Malware Detection Algorithm Based on the Attention Mechanism and ResNet,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1054-1060, 2020, 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).
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  • 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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  • G. E. Dahl, D. Yu, L. Deng, et al., "Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition", IEEE Transactions on Audio, Speech, and Language Processing, Vol.20, No.1, pp.30-42, 2012.
    O. Russakovsky, J. Deng, H. Su, et al., "ImageNet large scale visual recognition challenge", International Journal of Computer Vision, Vol.115, No.3, pp.211-252, 2015.
    P. Xu, Q. Miao, T. Liu, et al., "Line separation from topographic maps using regional color and spatial information", Proc. of International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp.3606-3612, 2018.
    P. Xu, L. Wang, Z. Guan, et al., "Evaluating brush movements for chinese calligraphy:A computer vision based approach", Proc. of International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp.1050-1056, 2018.
    A. Krizhevsky, "Learning multiple layers of features from tiny images", Tech Report, 2009.
    A. Krizhevsky, I. Sutskever, G. E. Hinton, et al., "ImageNet lassification with deep convolutional neural networks", Communications of The ACM, Vol.60, No.6, pp.84-90, 2017.
    H. Shrivastava, E. Bart, B. Price, et al., "Cooperative neural networks (CoNN):Exploiting prior independence structure for improved classification", 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, 2018.
    T. Wang, "Multi-value rule sets for interpretable classification with feature-efficient representations", Proc. of Advances in Neural Information Processing Systems, Montreal, Canada, pp.10835-10845, 2018.
    D. Dennis, C. Pabbaraju, H. V. Simhadri, et al., "Multiple instance learning for efficient sequential data classification on resource-constrained devices", Proc. of Advances in Neural Information Processing Systems, Montreal, Canada, pp.10953-10964, 2018.
    P. Zhao, Q. Miao, J. Song, et al., "Architectural style classification based on feature extraction module", IEEE Access, Vol.6, pp.52598-52606, 2018.
    K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition", Proc. of International Conference on Learning Representations, San Diego, CA, USA, 2015.
    C. Szegedy, W. Liu, Y. Jia, et al., "Going deeper with convolutions", Proc. of Computer Vision and Pattern Recognition, Boston, USA, 2015.
    K. He, X. Zhang, S. Ren, et al., "Delving deep into rectifiers:Surpassing human-level performance on imagenet classification", Proc. of IEEE International Conference on Computer Vision, Santiago, Chile, 2015.
    S. Ioffe and C. Szegedy, "Batch normalization:Accelerating deep network training by reducing internal covariate shift", Proc. of International Conference on Machine Learning, Lille, France, pp.448-456, 2015.
    R. Girshick, "Fast R-CNN", Proc. of IEEE International Conference on Computer Vision, Santiago, Chile, pp.1440-1448, 2015.
    R. Girshick, J. Donahue, T. Darrell, et al., "Rich feature hierarchies for accurate object detection and semantic segmentation", Proc. of Computer Vision and Pattern Recognition, Columbus, America, pp.580-587, 2014.
    K. He, X. Zhang, S. Ren, et al., "Spatial pyramid pooling in deep convolutional networks for visual recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.37, No.9, pp.1904-1916, 2015.
    H. J. Wang and S. Y. Zhang, "Robust object tracking via adaptive weight convolutional features", Journal of Xidian University, Vol.46, No.1, pp.117-123, 2019.
    L. Zhu, J. Zhao, Y. K. Fu, et al., "Deep learning algorithm for the segmentation of the interested region of an infrared thermal image", Journal of Xidian University, Vol.46, No.4, pp.107-114, 2019.
    J. G. Yang, X. L. Wang and S. G. Liu, "Spectral-spatial classification of hyperspectral images using deep boltzmann machines", Journal of Xidian University, Vol.46, No.3, pp.109-115, 2019.
    A. Vaswani, N. Shazeer, N. Parmar, et al., "Attention is all you Need", Proc. of Neural Information Processing Systems,, Long Beach, California,USA, pp.5998-6008, 2017.
    K. He, X. Zhang, S. Ren, et al., "Deep residual learning for image recognition", Proc. of Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, pp.770-778, 2016.
    T. Mikolov, K. Chen, G. S. Corrado, et al., "Efficient estimation of word representations in vector space", arXiv:1301.3781v3, 2013.
    Q. Luo, W. Xu, J. Guo, et al., "A study on the CBOW model's overfitting and stability", Proc. of Conference on Information and Knowledge Management, Shanghai, China, pp.9-12,2014.
    W. Cheng, C. Greaves, M. Warren, et al., "From n-gram to skipgram to concgram", International Journal of Corpus Linguistics, Vol.11, No.4, pp.411-433, 2006.
    J. Z. Feng, S. S. Song, Y. Z. Wang, et al., "Entity relation extraction based on improved attention mechanism", Acta Electronica Sinica, Vol.47, No.8, pp.1692-1700, 2019. (in Chinese)
    J. F. Pan, Y. Cao, Y. H. Dong, et al., "The community evolution event prediction based on attention deep random forest", Acta Electronica Sinica, Vol.47, No.10, pp.2050-2060, 2019. (in Chinese)
    F. Lyu, L. Y. Li, Victor Sheng, et al., "Multi-label image classification via coarse-to-fine attention", Chinese Journal of Electronics, Vol.28, No.6, pp.1118-1126, 2019.
    X. Y. Sun, L. Y. Ma and G. Y. Li, "Multi-vision attention networks for on-line red jujube grading", Chinese Journal of Electronics, Vol.28, No.6, pp.1108-1117, 2019.
    X. B. Wang, J. Zhang and S. H. Wang, "The cat's eye effect target recognition method based on visual attention", Chinese Journal of Electronics, Vol.28, No.5, pp.1080-1086, 2019.
    V. Mnih, N. Heess, A. Graves, et al., "Recurrent models of visual attention", Proc. of Neural Information Processing Systems, Montreal, Canada, pp.2204-2212,2014.
    D. Bahdanau, K. Cho, Y. Bengio, et al., "Neural machine translation by jointly learning to align and translate", Proc. of International Conference on Learning Representations, San Diego, CA, USA,2015.
    Ye Yang, "Research on detection method of malware based on behavior", Master Thesis, Xidian University, China, 2015.
    Suykens, Johan A K, and Joos Vandewalle, "Least squares support vector machine classifiers", Neural Processing Letters, Vol.9, No.3, pp.293-300, 1999.
    T. Hastie, S. Rosset, J. Zhu, et al., "Multi-class adaboost", Statistics and Its Interface, Vol.2, No.3, pp.349-360, 2009.
    L. Breiman, "Random forests", Machine Learning, Vol.45, No.1, pp.5-32, 2001.
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