Enhanced Deep Learning-Based Classification Approach for Detecting Data Leaks in Cloud Computing Environments

Abba Mohammed Yayaji, Badamasi Imam Ya’u

Abstract


In the realm of data security and privacy, organizations strive to safeguard sensitive information from unauthorized access and leakage. Data leakage, unintentional or malicious, can have serious implications, including financial losses, damage to reputation, and legal action. Traditional methods of data leakage detection often struggle to identify subtle patterns and anomalies. Whereas the network scenario that the study addresses needs the data leakage to be detected with highly consistent results for all the metrics, therefore, there is a need for advanced techniques that can effectively detect data leakage with high accuracy. This study suggests a deep learning approach for cloud computing that is based on data categorization and data leaking. Autoencoder (AE) architecture has been used to classify the input data. The developed AE classifier model was implemented using MATLAB R2023a. Support vector machines Convolutional neural networks, and artificial neural networks (SVM, CNN, and ANN) were used to compare the proposed model to current approaches. 


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