Abstract
In this paper, we demonstrate a new approach to virus detection. Extract information from a file’s Portable Executable (PE) structure to save storage costs compared to other types of features such as signatures, opcodes, or file strings, while still detect unknown malicious code. Use a deep learning network, namely the Deep Belief Network (DBN) model to classify and train data. The results show that the accuracy of the method is quite high, can reach over 97% for ten properties and over 95% for 15 properties, respectively.
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Nguyen, V.T., Hien, V.T., Tuan, L.D., Tiep, M.V., Anh, N.H., Vuong, P.T. (2020). A Computer Virus Detection Method Based on Information from PE Structure of Files Combined with Deep Learning Models. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2020. Communications in Computer and Information Science, vol 1306. Springer, Singapore. https://doi.org/10.1007/978-981-33-4370-2_9
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DOI: https://doi.org/10.1007/978-981-33-4370-2_9
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