Abstract
The malware uses morphological and polymorphic methods to evade detection, traditional malware recognition methods have gradually failed to cope with large and variable malware. To overcome drawbacks of static or dynamic analysis techniques, we merge the static and dynamic features as a new feature vector and form a feature matrix. In order to handle the effects of feature interactions we build a model for the interaction between tow feature vector in an efficient and effective manner, and apply Factorization Machine (FM) as the final classifier for malware classification because it can handle the feature sparsity effectively. The experimental results show that the method has a high accuracy for malware classification and a low false negative rate for malicious and benign dataset.
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Acknowledgments
This work is supported by Natural Science Foundation of China (NSFC), under grant number 61300220 and 61370227, and by Natural Science Foundation of Hunan Province of China, under grant number 2017JJ2100.
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Long, H., Li, Z., Jiang, F. (2020). Malware Classifications Based on Static-Dynamic Features and Factorization Machines. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981-15-8101-4_23
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DOI: https://doi.org/10.1007/978-981-15-8101-4_23
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