Abstract
Malware has now grown up to be one of the most important threats in the internet security. As the number of malware families has increased rapidly, a malware classification model needs to classify the samples from emerging malware families. In real-world environment, the number of malware samples varies greatly with each family and some malware families only have a few samples. Therefore, it is a challenge task to obtain a malware classification model with strong generalization ability by using only a few labeled malware samples in each family. In this paper, we propose an attention-based transductive learning approach to tackle this problem. To extract features from raw malware binaries, our approach first converts them into gray-scale images. After visualization, an embedding function is used to encode the images into feature maps. Then we build an attention-based Gaussian similarity graph to help transduct the label information from well-labeled instances to unknown instances. With end-to-end training, we validate our attention-based transductive learning network on a malware database of 11,236 samples with 30 different malware families. Comparing with state-of-the-art approaches, the experimental results show that our approach achieves a better performance.
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Acknowledgement
This work was supported by the National Key R&D Program of China(Grant No. 2018YFC1201102, Grant No. 2017YFB0802804) and Key Program of National Natural Science Foundation of China (Grant No. U1766215).
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Deng, L., Wen, H., Xin, M., Sun, Y., Sun, L., Zhu, H. (2020). Malware Classification Using Attention-Based Transductive Learning Network. In: Park, N., Sun, K., Foresti, S., Butler, K., Saxena, N. (eds) Security and Privacy in Communication Networks. SecureComm 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 336. Springer, Cham. https://doi.org/10.1007/978-3-030-63095-9_26
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DOI: https://doi.org/10.1007/978-3-030-63095-9_26
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