Research on Classification of Malicious Code Visualized Target Detection
Zipeng He, Yuntao Zhao, Jianjun Yang · 2024
With the generation of a large number of variant malicious codes in various forms, traditional detection techniques can no longer accurately detect these unknown malicious codes, and the malicious code visualization method can express the core of malicious codes in image features, which can more accurately identify the malicious codes. Firstly, we summarize the current malicious code detection methods, then introduce the malicious code visualization and the data set required for the experiment, and finally use the deep learning method to classify and detect the visualized malicious code images, aiming to test the effect of the malicious code visualization and a detection method that is based on the deep learning method, and help developing malicious code detection technology.