Research on malicious code detection based on deep learning

Abstract

With the popularization of the Internet and the rapid development of information technology, network attacks and threats have also increased significantly, especially the growth and update of malicious code, making network security increasingly face a huge threat. At the same time, the traditional malicious code detection technology is more difficult to follow due to the anti-malicious code shelling, obfuscation and other technologies. With the development of artificial intelligence and deep learning, malicious code detection has entered a new stage, greatly improving the accuracy and speed of detection. This paper first introduces the status quo and detection methods of malicious code detection, then introduces several deep learning algorithms and corresponding malicious code detection models, and details the structure and advantages of each detection model, including BP neural network, LSTM and convolutional neural network CNN. Finally, the problems faced by malicious code detection technology based on deep learning and the future development direction are further prospected.