https://repositorio.ufu.br/handle/123456789/17904 2026-04-24T17:32:40Z 2026-04-24T17:32:40Z https://repositorio.ufu.br/handle/123456789/48631 2026-04-11T06:31:31Z 2026-03-20T00:00:00Z

Title: Visualização e Interação no Tesouro Direto: Uma Análise de Usabilidade e Acessibilidade na Seção de Histórico de Preços e Taxas

2026-03-20T00:00:00Z https://repositorio.ufu.br/handle/123456789/48151 2026-02-05T06:25:18Z 2025-12-19T00:00:00Z

Title: Extração automatizada de chaves criptográficas em Ransomwares: uma abordagem forense baseada em AOB Abstract: Ransomware threats have emerged as a primary vector for cyberattacks, with incidence and the complexity of their techniques continuing to grow. Recent reports highlight the significant financial impacts of these attacks, with losses reaching millions of dollars per incident. Ransomware typically employs cryptographic mechanisms to encrypt files on compromised systems, making data recovery contingent on the payment of a ransom, generally associated with the receipt of the cryptographic key or a decryption tool. In this context, this work proposes an automated mechanism for extracting cryptographic keys from ransomware by analyzing volatile memory during malware execution. To gain greater control over the malware’s internal behavior and to enable reproducible experi ments, a custom ransomware sample was developed, inspired by notable strains such as WannaCry and LockBit. The proposed approach leverages AOB (Array of Bytes) signa tures to identify, in real-time, routines related to the generation and temporary storage of cryptographic keys in volatile memory. The experimental process involved static analysis with IDA Free, dynamic memory inspection with Cheat Engine, and the development of an automated tool, AOBTool. The results indicate that the cryptographic key can be located and extracted from memory before it is disposed of, within a timeframe shorter than that required to complete the file encryption process. This highlights the potential of the proposed technique as a valuable support tool for digital forensics and for mitigating ransomware-related incidents.

2025-12-19T00:00:00Z https://repositorio.ufu.br/handle/123456789/48103 2026-01-29T06:20:41Z 2025-09-23T00:00:00Z

Title: Análise Comparativa de Tecnologias de IA para o Desenvolvimento de Chatbots Especializados

2025-09-23T00:00:00Z https://repositorio.ufu.br/handle/123456789/48094 2026-01-28T06:29:02Z 2025-09-26T00:00:00Z

Title: GPT Teacher: desenvolvimento de um agente de LLM para programação assistida em ambiente VSCode Abstract: The computer programming teaching and learning process presents significant challenges that often result in comprehension difficulties and student demotivation. This paper presents the development and evaluation of GPT Teacher, an LLM-based agent for assisted programming integrated into the Visual Studio Code (VSCode) environment. The primary objective was to design and implement an LLM-based tool focused on programming education, capable of supporting the acquisition of programming competencies more effectively than generic coding assistants. The methodology involved creating a functional prototype based on a dual-agent architecture: a Diagnostic Agent, responsible for technical code analysis, and a Guidance Agent, tasked with translating this analysis into a constructive and educational dialogue for the student. The results of the functional validation demonstrate that the proposed approach is robust and promising, confirming the hypothesis that LLM agents, when structured within a specialized system, can serve as powerful allies in the programming teaching-learning process by reconciling technical rigor with pedagogical effectiveness.

2025-09-26T00:00:00Z