%0 Journal Article
%T 基于大数据的网络空间资产探测系统<br>Big Data-Based Cyberspace Asset Detection System
%A 陈明昊
%A 徐亚峰
%A 李琳
%A 王铭砾
%A 姬懿轩
%J Computer Science and Application
%P 170-179
%@ 2161-881X
%D 2025
%I Hans Publishing
%R 10.12677/csa.2025.153069
%X 随着网络空间规模的不断扩展以及网络攻击技术的日益复杂化&#65292;如何高效地识别、整理和分析网络资产成为网络安全领域的重要研究方向。本文提出了一种基于大数据技术的网络空间资产探测系统&#65292;涵盖子域名扫描与活跃性测试、URL资产整理与去重、IP识别、指纹扫描及智能辅助分析等功能模块。系统采用分布式存储和高性能计算技术&#65292;以布隆过滤器优化数据去重流程&#65292;提升探测效率与精度。实验表明&#65292;该系统能够在大规模网络资产探测中实现快速、准确的分析&#65292;适用于动态复杂的网络环境。<br>As cyberspace continues to expand and cyberattacks become increasingly sophisticated, efficiently identifying, organizing, and analyzing cyber assets has become a critical research focus in the field of cybersecurity. This paper proposes and implements a big data-based cyberspace asset detection system that incorporates modules for subdomain scanning, URL asset processing, IP detection, fingerprint recognition, and intelligent analysis. Leveraging distributed storage and high-performance algorithms such as Bloom filters, the system improves detection accuracy and efficiency. Experimental results demonstrate the system&#8217;s capability to perform rapid and accurate asset analysis in large-scale network environments, making it suitable for dynamic and complex scenarios.
%K 网络空间资产探测&#65292
%K 大数据技术&#65292
%K 布隆过滤器&#65292
%K 指纹识别&#65292
%K 网络安全<br>Cyberspace Asset Detection
%K Big Data Technology
%K Bloom Filter
%K Fingerprint Recognition
%K Cybersecurity
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=110236