Detecting Data Leakage in Cloud Storage Using Decision Tree Classification
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.
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