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This paper introduces a machine learning-based system that integrates real-time anomaly detection with dynamic user profiling, enabling the classification of employees into categories of low, medium, and high risk. The system was validated using a synthetic dataset, achieving exceptional accuracy across machine learning models, with XGBoost emerging as the most effective.<\/jats:p>","DOI":"10.3390\/fi17020093","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T08:36:19Z","timestamp":1739867779000},"page":"93","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Beyond Firewall: Leveraging Machine Learning for Real-Time Insider Threats Identification and User Profiling"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4793-158X","authenticated-orcid":false,"given":"Saif Al-Dean","family":"Qawasmeh","sequence":"first","affiliation":[{"name":"Department of Applied Science and Technology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8225-4180","authenticated-orcid":false,"given":"Ali Abdullah S.","family":"AlQahtani","sequence":"additional","affiliation":[{"name":"Department of Software Engineering (Cybersecurity Track), Prince Sultan University, Riyadh 12435, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,18]]},"reference":[{"key":"ref_1","unstructured":"Verizon (2024). 2024 Data Breach Investigations Report, Verizon. 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