RESUMO
This research aims to design and prototype a tool to perform intelligence on open sources (OSINT), specifically on official medical bulletins for the detection of false news. MedOSINT is a modular tool that can be adapted to process information from different medical official bulletins. From the processed information, intelligence is generated for decision making, validating the veracity of the COVID-19 news. The tool is compared with other options and it is verified that MedOSINT outperforms the current options when analyzing official bulletins. Moreover, it is complemented with an expert explanation provided by a Case-Based Reasoning (CBR) system. This is proved to be an ideal complement because it can find explanatory cases for an explanation-by-example justification.
RESUMO
The proper functioning of a hospital computer system is an arduous work for managers and staff. However, inconsistent policies are frequent and can produce enormous problems, such as stolen information, frequent failures, and loss of the entire or part of the hospital data. This paper presents a new method named EMRlog for computer security systems in hospitals. EMRlog is focused on two kinds of security policies: directive and implemented policies. Security policies are applied to computer systems that handle huge amounts of information such as databases, applications, and medical records. Firstly, a syntactic verification step is applied by using predicate logic. Then data mining techniques are used to detect which security policies have really been implemented by the computer systems staff. Subsequently, consistency is verified in both kinds of policies; in addition these subsets are contrasted and validated. This is performed by an automatic theorem prover. Thus, many kinds of vulnerabilities can be removed for achieving a safer computer system.
