基于大数据的网络空间资产探测系统
Big Data-Based Cyberspace Asset Detection System

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，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，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，以布隆过滤器优化数据去重流程，提升探测效率与精度。实验表明，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，适用于动态复杂的网络环境。
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’s capability to perform rapid and accurate asset analysis in large-scale network environments, making it suitable for dynamic and complex scenarios.