01. Universidade Federal Rural de Pernambuco - UFRPE (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/1
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Resultados da Pesquisa
Item Como o uso de Play Feature Delivery no Android pode ajudar na sustentabilidade digital(2023-09) Claudino, Yasmmin Maria Monteiro; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/0549149216731460With the increasing access to the internet via mobile devices among the less privileged classes in Brazil, digital sustainability becomes of growing importance. The aim of this study is to assess the impact of the Play Feature Delivery technology on mobile data consumption on Android devices. A parametric t-test was applied to evaluate significant differences between the averages of data amounts spent in Megabytes when downloading two applications. The result corresponded to a t-value of approximately 65.55 and the rejection of the null hypothesis. This finding not only underscores the technical importance for Android developers but also highlights its relevance in showing an improvement in mobile data usage, especially in an era where the democratization of access to information is vital. The research reinforces the idea that society should adapt to digital resources, optimizing data usage. To reach these conclusions, two mobile applications focused on first aid guidelines were developed and analyzed. The main advantage observed was the reduction in mobile data consumption, validating the efficacy of Play Feature Delivery compared to conventional applications.Item Detecção de aplicativos maliciosos no sistema operacional android por meio de análise estática automatizada(2017-09-06) Silva, Diógenes José Carvalho da; Lins, Fernando Antonio Aires; http://lattes.cnpq.br/2475965771605110; http://lattes.cnpq.br/0986435158192139The mobile applications platform known as Android provides a wide an open environment of application development to all kinds of software, however this freedom can bring possible software security vulnerabilities that can be used unfortunately to create threats to the operation system. There are vulnerabilities that comes from software and hardware that allows the creation of threats called: spyware, diverse kinds of malware, and with raising popularity, the ransomware. In this case is necessary to build application analysis to find out threats that are increasing in size and complexity. To accomplish this task, this research proposes a technique that combines multiple strategies to orchestrate a new technique that can detect threats and vulnerabilities inside applications developed to the Android mobile operational system. The strategy combines automatic static analysis and threat profile identification by metadata from an external source. Using techniques like web crawling to collect metadata from application stores, we generated a data set with 1000 applications, which 500 are infected and 500 aren't, using balancing technique such as super sampling, extraction and selection of features like: TF-IDF, frequency of terms, feature conversion from nominal to binary and normalization. Using the generated data set to create classification models with the most used machine learning algorithms used by other researchers, we could provide precision metrics, false positives, and false negatives at acceptable rates, comparable to other researches that presents the same performance metrics.