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All Journal Internet of Things and Artificial Intelligence Journal
Arkam, Andi Iksan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Content Blocking Method To Reduce False Positives Based On Machine Learning Arkam, Andi Iksan; Yahya, Muhammad; Wahid, Abdul
Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v5i2.935

Abstract

This study presents an experimental approach to enhance content-blocking systems by integrating machine learning with domain classification and Pi-hole DNS server technology. While traditional blocking mechanisms often result in false positives—legitimate domains mistakenly blocked—this research aims to mitigate such issues. By implementing various testing scenarios, including TF-IDF and N-gram feature extraction with and without preprocessing, the study evaluates the classification performance using the Naive Bayes algorithm. The results reveal the highest accuracy of 84% achieved with the N-gram method without preprocessing. This integrated approach shows promise in improving the precision of ad and website blocking mechanisms.
Co-Authors Abdul Wahid Muhammad Yahya