Research Article
Design of Malicious Code Detection System Based on Convolutional Neural Network
@INPROCEEDINGS{10.1007/978-3-031-31275-5_12, author={Yumeng Wu and Jianjun Zeng and Zhenjiang Zhang and Wei Li and Zhiyuan Zhang and Yang Zhang}, title={Design of Malicious Code Detection System Based on Convolutional Neural Network}, proceedings={Smart Grid and Internet of Things. 6th EAI International Conference, SGIoT 2022, TaiChung, Taiwan, November 19-20, 2022, Proceedings}, proceedings_a={SGIOT}, year={2023}, month={5}, keywords={convolutional neural network malicious code detection network security deep learning}, doi={10.1007/978-3-031-31275-5_12} }- Yumeng Wu
Jianjun Zeng
Zhenjiang Zhang
Wei Li
Zhiyuan Zhang
Yang Zhang
Year: 2023
Design of Malicious Code Detection System Based on Convolutional Neural Network
SGIOT
Springer
DOI: 10.1007/978-3-031-31275-5_12
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.
