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All Journal Journal of Applied Geospatial Information JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics Jurnal Info Sains : Informatika dan Sains Recht Studiosum Law Review Cosmic Jurnal Teknik
Harahap, Parlindungan
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Physics Social Sciences

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Journal : Journal of Information Systems and Informatics

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Detecting Data Leakage in Cloud Storage Using Decision Tree Classification Harahap, Parlindungan; Hasibuan, Muhammad Siddik
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1215

Abstract

Data leakage in cloud storage systems poses a significant security threat, potentially leading to unauthorized access, loss of sensitive information, and operational disruptions. This research proposes a classification model for detecting potential data leakage incidents using the Decision Tree algorithm. The dataset, obtained from the Kaggle public repository, contains user activity logs representing both normal and anomalous behaviors in cloud storage environments. Several preprocessing steps were applied to improve model quality, including handling missing values, removing outliers, and converting categorical data into numerical form. Hyperparameter optimization was performed using GridSearchCV to determine the best configuration for the Decision Tree classifier. Experimental results demonstrate that the optimized model achieved high classification performance, with an accuracy of 70,84%, a precision of 55% for the data leakage class, and an F1-score of 40%. The analysis also highlights the significance of certain features, such as multi-factor authentication usage and access to confidential data, in predicting potential leakage events. This study provides a theoretical contribution by \establishing a robust methodology for applying Decision Tree algorithms to a novel cloud security dataset, offering a scalable and interpretable framework for automated threat detection.
Co-Authors Abduh Aqil, Nabil Amalia, Endang Dani Hamdani, Dani Ilka Zufria Jabbar, Teuku Muhammad Qashmal Muhammad Siddik Hasibuan Nugraha, Ucu Sapanji, R. A. E. Virgana Targa