Gerenciamento de dados de serviços de reputação de fontes abertas

Abstract

The increasing use of the Internet as a knowledge seeking tool presents the challenge of distinguishing reliable information from malicious content. This study addresses the management of data from open-source reputation services, which classify IPs and URLs based on their potential maliciousness. The study proposes an approach to efficiently collect and analyze this information. Initially, the work analyzes data from various reputation services, identifying characteristics that allow for more accurate classification. Subsequently, scripts are used to verify and manage this data, prioritizing the maintenance of active addresses and discarding irrelevant or obsolete information. The results indicate that the proposed approach not only improves the efficiency of reputation information management but also enhances the consistency and credibility of this data. The contributions of the study include the identification of open-source reputation services and the development of a methodology that retains only active addresses on the Internet, reducing false positives and improving threat detection. It is concluded that improved management of reputation information can significantly benefit cybersecurity by providing a more reliable database through open-source reputation services for detecting and preventing malicious activities.