Abstract
With the rapid development of Internet of things, cloud computing, edge computing and other technologies, malicious code attacks users and even enterprises more and more frequently with the help of software and system security vulnerabilities, which poses a serious threat to network security. The traditional static or dynamic malicious code detection technology is difficult to solve the problem of high-speed iteration and camouflage of malicious code. The detection method based on machine learning algorithm and data mining idea depends on manual feature extraction, and can not automatically and effectively extract the deeper features of malicious code. In view of the traditional malicious code detection methods and the related technologies of deep learning, this paper integrates deep learning into the dynamic malicious code detection system, and proposes a malicious code detection system based on convolutional neural network.
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This research is supported by the National Natural Science Foundation of China (NO. 62173026).
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Wu, Y., Zeng, J., Zhang, Z., Li, W., Zhang, Z., Zhang, Y. (2023). Design of Malicious Code Detection System Based on Convolutional Neural Network. In: Deng, DJ., Chao, HC., Chen, JC. (eds) Smart Grid and Internet of Things. SGIoT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-031-31275-5_12
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DOI: https://doi.org/10.1007/978-3-031-31275-5_12
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