Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Spyware Integrated with Prediction Models for Monitoring Corporate Computers

Version 1 : Received: 27 January 2023 / Approved: 31 January 2023 / Online: 31 January 2023 (08:59:39 CET)

How to cite: Noetzold, D.; Rossetto, A.G.D.M.; Leithardt, V.R.Q. Spyware Integrated with Prediction Models for Monitoring Corporate Computers. Preprints 2023, 2023010580. https://doi.org/10.20944/preprints202301.0580.v1 Noetzold, D.; Rossetto, A.G.D.M.; Leithardt, V.R.Q. Spyware Integrated with Prediction Models for Monitoring Corporate Computers. Preprints 2023, 2023010580. https://doi.org/10.20944/preprints202301.0580.v1

Abstract

Technological innovations and the expansion of Internet access have produced significant changes in the configurations of organizations and, consequently, in the relationships between employees and employers. This new scenario generates the need for greater monitoring in the workplace in order to control inappropriate behavior or situations that may generate misfortunes. Two important problems faced are the dissemination of hate through networks and data leakage that can have social, psychological, and financial impacts. Thus, monitoring tools can be incorporated to assist in surveillance, and thus ensure the achievement of organizational objectives. This paper presents a workplace computer monitoring solution that integrates Spyware techniques, and text sentiment classification, along with a distributed microservices architecture, which aims to collect a range of information and generate alerts to managers regarding hate speech and vulnerabilities. Preliminary tests have been conducted to evaluate the performance of Spyware integrated with prediction models.

Keywords

Electronic monitoring; hate speech; data leakage; prediction

Subject

Computer Science and Mathematics, Information Systems

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