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Abstract：
In today\'s digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices.
摘要：
在今天的数字景观，组织面临重大挑战，包括敏感数据泄露和仇恨言论的泛滥,这两者都可能导致严重的后果，如经济损失，声誉受损，以及对员工的心理影响。这项工作考虑了一个全面的解决方案，使用微服务架构来有效地监控组织内的计算机使用情况。该方法结合了间谍软件技术以从员工计算机和用于警报管理的Web应用程序捕获数据。系统检测到数据泄漏，可疑行为,和仇恨言论，通过高效的数据捕获和预测建模。因此,本文对SpringBoot和Quarkus的性能进行了比较分析，注重客观指标和定量统计。通过利用计算机科学界公认的工具和基准，该研究深入了解了这两个平台之间的性能差异。Quarkus在SpringBoot上的实现展示了实质性的改进：内存使用量减少了80%，CPU使用量减少了95%，系统正常运行时间减少了119%。该解决方案提供了一个强大的框架，可通过主动监控和预测分析来增强组织安全性并减轻潜在威胁，同时还指导开发人员和软件架构师做出明智的技术选择。
