An Environment-Specific Prioritization Model for Information-Security Vulnerabilities Based on Risk Factor Analysis

Vulnerabilities represent a constant and growing risk for organizations. Their successful exploitation compromises the integrity and availability of systems. The use of specialized tools facilitates the vulnerability monitoring and scanning process. However, the large amount of information transmitted over the network makes it difficult to prioritize the identified vulnerabilities based on their severity and impact. This research aims to design and implement a prioritization model for detecting vulnerabilities based on their network environment variables and characteristics. A mathematical prioritization model was developed, which allows for calculating the risk factor using the phases of collection, analysis, and extraction of knowledge from the open information sources of the OSINT framework. The input data were obtained through the Shodan REST API. Then, the mathematical model was applied to the relevant information on vulnerabilities and their environment to quantify and calculate the risk factor. Additionally, a software prototype was designed and implemented that automates the prioritization process through a Client-Server architecture incorporating data extraction, correlation, and calculation modules. The results show that prioritization of vulnerabilities was achieved with the information available to the attacker, which allows evaluating the overexposure of information from organizations. Finally, we concluded that Shodan has relevant variables that assess and quantify the overexposure of an organization's data. In addition, we determined that the Common Vulnerability Scoring System (CVSS) is not sufficient to prioritize software vulnerabilities since the environments where they reside have different characteristics.

Automated summary

This research aims to design and implement a prioritization model for detecting vulnerabilities based on their network environment variables and characteristics, utilizing a mathematical model algorithm to calculate the risk factor for improved prioritization efficiency.