Navegando por Orientadores "SALES JUNIOR, Claudomiro de Souza de"

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Acesso aberto (Open Access)
Abordagem Inteligente com Combinação de Características Estruturais para Detecção de Novas Famílias de Ransomware
(Universidade Federal do Pará, 2024-03-22) MOREIRA, Caio Carvalho; SALES JUNIOR, Claudomiro de Souza de; País de Nacionalidade Brasi
Ransomware is a malicious software that aims to encrypt user files and demand a ransom to unlock them. It is a cyber threat that can cause significant financial damage, as well as compromise privacy and data integrity. Although signature-based detection scanners commonly combat this threat, they fail to identify unknown ransomware families (variants). One method to detect new threats without the need to execute them is static analysis, which inspects the code and structure of the software, along with classification through intelligent approaches. The Detection of New Ransomware Families (DNFR) can be evaluated in a realistic and challenging scenario by categorizing and isolating families for training and testing. Hence, this thesis aims to develop an effective static analysis model for DNFR, which can be applied in Windows systems as an additional security layer to check executable files upon receipt or before execution. Early ransomware detection is essential to reduce the likelihood of a successful attack. The proposed approach comprehensively analyzes executable binaries, extracting and combining various structural features, and distinguishes them between ransomware or benign software employing a soft voting model comprising three machine learning techniques: Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGB). Results for DNFR demonstrated an average accuracy of 97.53%, precision of 96.36%, recall of 97.52%, and F-measure of 96.41%. Additionally, scanning and predicting individual samples took an average of 0.37 seconds. This performance indicates success in quickly identifying unknown ransomware variants and adapting the model to the constantly evolving landscape, suggesting its applicability in antivirus protection systems, even on resource-limited devices. Therefore, the method offers significant advantages and can assist developers of ransomware detection systems in creating more resilient, reliable, and fast-response solutions.
Acesso aberto (Open Access)
Development of machine learning-based frameworks to predict permeability of peptides through cell membrane and blood-brain barrier
(Universidade Federal do Pará, 2024-03-27) OLIVEIRA, Ewerton Cristhian Lima de; LIMA, Anderson Henrique Lima e; http://lattes.cnpq.br/2589872959709848; https://orcid.org/0000-0002-8451-9912; SALES JUNIOR, Claudomiro de Souza de; http://lattes.cnpq.br/4742268936279649
Peptides comprise a versatile class of biomolecules with diverse physicochemical and structural properties, in addition to numerous pharmacological and biotechnological applications. Some groups of peptides can cross biological membranes, such as the cell membrane and the human blood-brain barrier. Researchers have explored this property over the years as an alternative to developing more powerful drugs, given that some peptides can also be drug carriers. Although some machine learning-based tools have been developed to predict cell-penetrating peptides (CPPs) and blood-brain barrier penetrating peptides (B3PPs), some points have not yet been explored within this theme. These points encompass the use of dimensionality reduction (DR) techniques in the preprocessing stage, molecular descriptors related to drug bioavailability, and data structures that encode peptides with chemical modifications. Therefore, the primary purpose of this thesis is to develop and test two frameworks based on DR, the first one to predict CPPs and the second to predict B3PPs, also evaluating the molecular descriptors and data structure of interest. The results of this thesis show that for the prediction of penetration in the cell membrane, the proposed framework reached 92% accuracy in the best performance in an independent test, outperforming other tools created for the same purpose, besides evidencing the contribution between the junction of molecular descriptors based on amino acid sequence and those related to bioavailability and cited in Lipinski’s rule of five. Furthermore, the prediction of B3PPs by the proposed framework reveals that the best model using structural, electric, and bioavailability-associated molecular descriptors achieved average accuracy values exceeding 93% in the 10-fold cross-validation and between 75% and 90% accuracy in the independent test for all simulations, outperforming other machine learning (ML) tools developed to predict B3PPs. These results show that the proposed frameworks can be used as an additional tool in predicting the penetration of peptides in these two biomembranes and are available as free-touse web servers.