Work

Automated OSINT Techniques for Digital Asset Discovery and Cyber Risk Assessment

Year: 2025

Type: article

Abstract: Cyber threats are becoming increasingly sophisticated, especially in distributed infrastructures where systems are deeply interconnected. To address this, we developed a framework that automates how organizations discover their digital assets and assess which ones are the most at risk. The approach integrates diverse public information sources, including WHOIS records, DNS data, and SSL certificates, into a unified analysis pipeline without relying on intrusive probing. For risk scoring we applied Gradient Boosted Decision Trees, which proved more robust with messy real-world data than other models we tested. DBSCAN clustering was used to detect unusual exposure patterns across assets. In validation on organizational data, the framework achieved 93.3% accuracy in detecting known vulnerabilities and an F1-score of 0.92 for asset classification. More importantly, security teams spent about 58% less time on manual triage and false alarm handling. The system also demonstrated reasonable scalability, indicating that automated OSINT analysis can provide a practical and resource-efficient way for organizations to maintain visibility over their attack surface.

Source: Computers

DOI: 10.3390/computers14100430

Authors: Tetiana Babenko, Kateryna Kolesnikova, Olga Abramkina, Yelizaveta Vitulyova

Institutions: International Information Technologies University, Almaty University of Power Engineering and Telecommunications, International Engineering and Technological University

Language: English

Topic: Web Application Security Vulnerabilities

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,172