A Spyware Platform and Predictive Models for Monitoring Computers

Abstract

Organizations increasingly rely on computer systems, but they face significant challenges such as sensitive data leaks and the proliferation of hate speech. These issues can lead to financial losses, reputational harm, and psychological impacts on employees. Existing solutions often address specific aspects, such as monitoring productivity or detecting hate speech in isolation, without providing an integrated approach. This article proposes a microservices-based solution that combines spyware techniques for monitoring computer activity and predictive models to detect hate speech, with a focus on scalability and real-time alert management. Methodologically, the solution employs machine learning models, such as BERT, to enhance the accuracy of hate speech detection, achieving an average accuracy of 87%. Additionally, it incorporates a modular architecture leveraging RabbitMQ and Redis to ensure efficient data processing and alert delivery. Performance evaluations highlight the system's ability to promptly identify suspicious behaviors and data leaks, addressing the shortcomings of existing solutions by offering a unified framework that integrates monitoring, security, and predictive capabilities.

Keywords: Spyware, Computer Systems Security, Hate Speech Detection, Machine Learning, Real-Time Alerts