中圖分類號：TP309;TP311.5文獻(xiàn)標(biāo)識碼：ADOI:10.19358/j.issn.2097-1788.2024.05.003
引用格式：高偉波,徐炳雪,李仲琴.數(shù)據(jù)科學(xué)的可視化惡意軟件分析技術(shù)在檔案數(shù)字化安全管理系統(tǒng)中的應(yīng)用［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理，2024,，43（5）：18-26.

Application of visual malware analysis technology of data science in archive digital security management system

Abstract： The application of data science′s visual malware analysis technique is an important and innovative technique in the current information security field. This technology combines methods of data analysis, machine learning and visualization, and aims to improve the detection and response capability of potential threats in archive digitization systems. This paper discusses the application of data science-based visual malware analysis technology in archive digitization security management through practice, and constructs a neural network-based malware detection model by combining visual data sets. The advantages of using visual mapping to analyze the detection effect and iterative trend of malware are more efficient and readable than the traditional means of data stripping, and in the process of analysis, it can more quickly and accurately respond to the evolving threats, which provides a strong support for the security of digital archives.

Key words : archive digitization; data science; malware threats; neural networks