Abstract—In the context of the increasing complexity of cyberspace, accurately and efficiently mapping network assets has become paramount for effective cybersecurity measures. This study presents a novel cyberspace mapping system that leverages multi-source data aggregation to enhance the discovery of network assets. To resolve inconsistencies in query syntax across various tools, the system employs a unified query framework utilizing the Aho-Corasick algorithm, which significantly enhances syntax conversion and matching efficiency. The system amalgamates data from multiple cyberspace mapping engines and conducts rigorous credibility assessments to ensure the reliability and quality of the data. Real-time synchronization, achieved through multithreading, ensures data remains current and comprehensive, thereby facilitating accurate and timely cybersecurity analyses. Experimental evaluations conducted on 2000 entries yielded a high credibility score of 99.9337% and demonstrated an average accuracy improvement of 3.33% compared to individual tools. Furthermore, the system incorporates advanced visualization tools, such as radar charts and tree diagrams, which support effective data interpretation and aid in decision-making processes. This comprehensive system not only optimizes query standardization, data aggregation, and credibility assessment but also enhances visualization capabilities, creating a strong foundation for future integrations of deep learning technologies and enabling real-time responses to evolving cybersecurity challenges.
 
Keywords—cyberspace surveying and mapping, multi-source data aggregation, credibility assessment, network assets


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