数据科学的可视化恶意软件分析技术在档案数字化安全管理系统中的应用
Application of visual malware analysis technology of data science in archive digital security management system
高伟波 1徐炳雪 2李仲琴 1赫明春3
作者信息
- 1. 江西省地质局核地质大队,江西 鹰潭 335001
- 2. 鹰潭开放大学 信息中心,江西 鹰潭 335001
- 3. 浙江大学 计算机学院,浙江 杭州 310013
- 折叠
摘要
数据科学的可视化恶意软件分析技术是当前信息安全领域中的重要的创新技术,旨在提高对档案数字化系统中潜在威胁的检测和应对能力.系统论述了基于数据科学的可视化恶意软件分析技术在档案数字化安全管理系统中的应用,并结合可视化数据集构建了基于神经网络的恶意检测模型.使用可视化绘图对恶意软件的检出效果和迭代趋势进行分析,其优点对比传统的数据剥离手段更加高效并更具可读性,且在分析的过程中能够迅速、准确地应对不断演进的威胁,为数字化档案的安全提供了有力的支持.
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.
关键词
档案数字化/数据科学/恶意软件威胁/神经网络Key words
archive digitization/data science/malware threats/neural networks引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据