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All Journal Journal La Multiapp
Abed, Israa Jihad
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering

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Using Machine Learning to Detect Unauthorized Access in Database's Log Files Abed, Israa Jihad
Journal La Multiapp Vol. 5 No. 6 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i6.1538

Abstract

The paper investigates the use of machine learning techniques to detect unauthorized access in database log files. Results show that most algorithms of supervised machine learning performed well in identifying normal cases but struggled to detect anomalies, with the exception of Naïve Bayes and Random Forest which gave mediocre results by identifying one out of twenty anomalies. In the semi-supervised machine learning methods, Local Outlier Factor showed an accuracy of 0.98 in detecting normal cases and 0.7 in detecting anomalies. One Class Support Vector Machine had an accuracy of 0.89 for normal cases and 0.05 for anomalies, while Isolation Forest had an accuracy of 0.98 for normal cases and 0.0 for anomalies. These findings suggest that semi-supervised techniques may be more effective in detecting unauthorized access in database log files.
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