arXiv subjects

Valderi Reis Quietinho Leithardt

Publications and source records attributed to Valderi Reis Quietinho Leithardt.

4 recordsLinked to original sources

arXiv2026

A microservices-based endpoint monitoring platform with predictive NLP models for real-time security and hate-speech risk alerting

Organizations increasingly depend on endpoint devices and corporate communication channels, yet they still face critical risks such as sensitive data leakage, suspicious user behavior, and the circulation of hateful or harmful language in workplace contexts. Current solutions frequently address these issues in isolation (e.g., productivity tracking, data loss prevention, or hate-speech detection), limiting correlation across signals and delaying incident response. This work proposes a unified, microservices-based platform that collects endpoint telemetry and applies predictive natural language processing models to support real-time security and compliance alerting. The architecture is modular and scalable, relying on RabbitMQ for event ingestion and routing and Redis for low-latency data access and alert delivery. For text classification, transformer-based models such as BERT are evaluated for hate-speech risk detection, achieving an average accuracy of 87\%. Experimental results indicate that the proposed platform can promptly surface indicators of data exfiltration and policy violations while centralizing alert management, providing an integrated framework that combines monitoring, security analytics, and predictive capabilities.

cs.CR↗

arXiv2026

Fundamentals, Recent Advances, and Challenges Regarding Cryptographic Algorithms for the Quantum Computing Era

This book arises from the need to provide a clear and up-to-date overview of the impacts of quantum computing on cryptography. The goal is to provide a reference in Portuguese for undergraduate, master's, and doctoral students in the field of data security and cryptography. Throughout the chapters, we present fundamentals, we discuss classical and post-quantum algorithms, evaluate emerging patterns, and point out real-world implementation challenges. The initial objective is to serve as a guide for students, researchers, and professionals who need to understand not only the mathematics involved, but also its practical implications in security systems and policies. For more advanced professionals, the main objective is to present content and ideas so that they can assess the changes and perspectives in the era of quantum cryptographic algorithms. To that end, the text's structure was designed to be progressive: we begin with essential concepts, move on to quantum algorithms and their consequences (with emphasis on Shor's algorithm), present issues focusing on "families" of post-quantum schemes (based on lattices, codes, hash functions, multivariate, isogenies), analyze the state of the art in standardization (highlighting the NIST process), and finally, discuss migration, interoperability, performance, and cryptographic governance. We hope that this work will assist in the formation of critical thinking and informed technical decision-making, fostering secure transition strategies for the post-quantum era.

cs.CR↗

arXiv2025

Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators

Surface contamination on electrical grid insulators leads to an increase in leakage current until an electrical discharge occurs, which can result in a power system shutdown. To mitigate the possibility of disruptive faults resulting in a power outage, monitoring contamination and leakage current can help predict the progression of faults. Given this need, this paper proposes a hybrid deep learning (DL) model for predicting the increase in leakage current in high-voltage insulators. The hybrid structure considers a multi-criteria optimization using tree-structured Parzen estimation, an input stage filter for signal noise attenuation combined with a large language model (LLM) applied for time series forecasting. The proposed optimized LLM outperforms state-of-the-art DL models with a root-mean-square error equal to 2.24$\times10^{-4}$ for a short-term horizon and 1.21$\times10^{-3}$ for a medium-term horizon.

cs.LG↗

arXiv2024

Prichain II: CloudGuardian Cloud Security Proposal with Blockchain

With the advancement of cloud computing, data storage, and security have become crucial. The growing adoption of cloud services by companies, accompanied by increased threats from cybersecurity, highlights the importance of privacy and ownership of user data. Between 2022 and 2023, there has been an increase of around 48% in cloud security threats, emphasizing the urgent need for strong security solutions. To face these challenges, in this project, we propose integrating the Ethereum network's blockchain technology with a database located in the PostgreSQL cloud. The proposed solution aims to provide bidirectional data synchronization and strict control of access mechanisms. Blockchain technology ensures immutability and transparency of transactions, while PostgreSQL provides efficient and scalable storage. Through rigorous testing in an adaptive traffic control scenario, the results obtained indicate that this solution offers a significantly high level of security due to the decentralization of data, confirming that this solution is effective, and making it a powerful new option to improve security in cloud environments. In conclusion, the solution proposed in this project not only increases information security but also demonstrates the practical feasibility of integrating blockchain with cloud relational databases. This two-way alignment improves protection against cyberattacks and ensures that user data is protected from unauthorized access and malicious changes.

cs.CR↗