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Parthasarathy, Kirthikesh (2024) An Enhanced Version of Data Classification based on Confidentiality for Cloud Security. Masters thesis, Dublin, National College of Ireland.
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Abstract
The rapid proliferation of cloud technologies has motivated the organizations to store the data over cloud platforms since they offer high scalability and better performance in terms of software as a service. However, the increased deployment of cloud services has also increased the need for ensuring the protection of sensitive information stored in cloud servers. Privacy protection has become one of the critical aspects for various organizations that move their data to the cloud. Since the data can be of different types, the security requirements for data protection also vary. The crucial issue of securing data in cloud environments is addressed in this work by deploying an effective classification framework. This paper presents the design of a unique classification framework for securing the confidential data stored in the cloud. The classification model is developed in this work using the RandomForest (RF) classifier and the model is trained using the data features. The essential features are extracted using a hybrid CNN-LSTM model and a K-means SMOTE algorithm is used for addressing the class imbalance issues. Furthermore, the trained model is deployed into a Container as a Service (CaaS) environment and the deployed model is known as AUG-ConvoLSTM-RF. The model combines both data augmentation and Natural Language Processing (NLP) techniques for accurately classifying the data as confidential and non-confidential. The efficacy of the AUG-ConvoLSTM-RF model was experimentally evaluated and results show that the model exhibits an excellent classification accuracy of 84.36 % compared to other existing models.
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