USAGE OF OPEN-SOURCE INTELLIGENCE FOR SECURITY OF CRITICAL INFRASTRUCTURE

Abstract

B a c k g r o u n d . In the metter of critical infrastructure, it refers to the systems and assets that are essential for the functioning of modern society and the economy. These sectors include energy, transportation, elecommunications, healthcare, and water supply, all of which are crucial for national security and public well-being. Disruptions in these infrastructures can lead to decent amount of social and economic vital consequences. With the technologies happening to become more advanced, critical infrastructure security systems have become more complex and affiliated. Alterations in example being smart grids, automated transportation systems, and sophisticated communication networks have enhanced efficiency but also increased vulnerabilities. The convergence of digital and physical systems makes these sectors more exposed to risks like cyberattacks, natural disasters, terrorism, and other threats. This growing complexity emphasizes the need for governments and organizations to prioritize the protection of these vital infrastructures.

M e t h o d s . In this research, we developed a mathematically rigorous approach to OSINT in the protection of critical infrastructure, improving on existing methods by providing a structured model for threat detection, vulnerability assessment, and risk calculation. The proposed method employs mathematical representations and probability functions, ensuring a more accurate analysis of threat information and vulnerability scoring. This advancement enables more precise mitigation strategies and better response coordination. While existing OSINT methods rely heavily on unstructured data collection and analysis, our approach introduces a mathematical foundation for data gathering and threat evaluation, providing several key improvements, such as Mathematical Representation of Data; Probabilistic Threat Detection and Vulnerability and Risk Assessment with Weighted Metrics.

R e s u l t s . The study's findings underscore the value of a quantitative OSINT model in critical infrastructure security, demonstrating improvements in accuracy, speed, and decision-making. By reducing ambiguity through probabilistic risk assessments, the model minimizes unnecessary alerts and focuses on actionable threats. Scalability testing showed the model could handle large datasets effectively without overwhelming analysts. Finally, objective risk assessments were validated as enhancing decision-making processes, thus proving beneficial in real-time threat detection and mitigation. The model provides a solid foundation for continuously evolving OSINT practices and suggests potential for further optimization by minimizing risk and balancing mitigation efforts through a defined objective function.

C o n c l u s i o n s . After all conducted analytical works, we could definitely say that this mathematical model demonstrates how OSINT can be systematically used to enhance the security of critical infrastructure by assessing vulnerabilities, detecting threats, calculating risk, and applying targeted mitigation strategies. It leverages data collection from open sources, threat analysis, and continuous feedback to ensure that infrastructure systems are resilient to evolving risks.