Version 1
: Received: 22 May 2024 / Approved: 23 May 2024 / Online: 23 May 2024 (07:53:58 CEST)
How to cite:
Rossetto, A. G. D. M.; Noetzold, D.; Silva, L. A.; Leithardt, V. R. Q. Enhancing Corporate Security: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models. Preprints2024, 2024051504. https://doi.org/10.20944/preprints202405.1504.v1
Rossetto, A. G. D. M.; Noetzold, D.; Silva, L. A.; Leithardt, V. R. Q. Enhancing Corporate Security: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models. Preprints 2024, 2024051504. https://doi.org/10.20944/preprints202405.1504.v1
Rossetto, A. G. D. M.; Noetzold, D.; Silva, L. A.; Leithardt, V. R. Q. Enhancing Corporate Security: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models. Preprints2024, 2024051504. https://doi.org/10.20944/preprints202405.1504.v1
APA Style
Rossetto, A. G. D. M., Noetzold, D., Silva, L. A., & Leithardt, V. R. Q. (2024). Enhancing Corporate Security: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models. Preprints. https://doi.org/10.20944/preprints202405.1504.v1
Chicago/Turabian Style
Rossetto, A. G. D. M., Luis Augusto Silva and Valderi Reis Quietinho Leithardt. 2024 "Enhancing Corporate Security: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models" Preprints. https://doi.org/10.20944/preprints202405.1504.v1
Abstract
Due to the increasing use of computer equipment, institutions and companies face challenges, such as sensitive data leaks and the spread of hate speech, which have severe consequences for organizations and their employees. Addressing these challenges is essential to avoid financial losses, reputational damage, and impacts on the psychological health of those involved. This paper presents a solution based on microservices for monitoring computers used by employees in organizations, including capturing information from the equipment using spyware techniques and a web application for managing alerts. The solution seeks to detect data leaks, suspicious behaviour, and hate speech. The results of the evaluation indicate that the proposed solution has an efficient data capture time and can identify unwanted behaviour in a short period. The solution also includes applying prediction models to detect hate speech, achieving an average accuracy of approximately 87%. The performance, scalability, and security evaluation demonstrate that the solution is suitable for dealing with data leakage and hate speech challenges in the corporate environment.
Keywords
electronic monitoring; hate speech; data leakage; prediction; microservices
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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