TY - JOUR
T1 - Enhancing Monitoring Performance
T2 - A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models
AU - Rossetto, Anubis Graciela de Moraes
AU - Noetzold, Darlan
AU - Silva, Luis Augusto
AU - Leithardt, Valderi Reis Quietinho
N1 - Funding Information:
The authors would like to acknowledge the Portuguese FCT program, Center of Technology and Systems (CTS) UIDB/00066/2020/UIDP/00066/2020, for partially funding the research.
Publisher Copyright:
© 2024 by the authors.
PY - 2024/6/28
Y1 - 2024/6/28
N2 - 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.
AB - 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.
KW - data leakage
KW - electronic monitoring
KW - hate speech
KW - microservices
KW - prediction
UR - http://www.scopus.com/inward/record.url?scp=85198323859&partnerID=8YFLogxK
U2 - 10.3390/s24134212
DO - 10.3390/s24134212
M3 - Article
C2 - 39000991
AN - SCOPUS:85198323859
SN - 1424-8220
VL - 24
JO - Sensors
JF - Sensors
IS - 13
M1 - 4212
ER -