Abstract
Cloud computing provides more beneficial services to its users with limited cost. Cloud is prone to many threats, and one of the major threats is the malicious insider threat. Detection of malicious insider threats is more challenging, and many cloud datasets are available to detect a malicious insider. In real-time data collection, the data set is prone to a class imbalance problem. Minority class related to insider threat events has a smaller number of instances, whereas majority class related to non-insider threats has a minimum number of instances. Supervised classification techniques provide a better result for the classification of the majority class and a less accurate result for the minority class. Classification without treating the imbalanced class data results in adverse effects in prediction. In this paper, different sampling techniques are implemented to accurately handle the imbalanced class data to detect malicious insider threats in cloud computing. The performance of different sampling techniques is compared by implementing Support Vector Machine (SVM) algorithm using the performance metrics such as accuracy, f-score, precision and recall.
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Acknowledgements
This work is supported by Centre for Cyber Intelligence (CCI), DST-CURIE-AI-Phase II Project, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamilnadu, India - 641027.
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Padmavathi, G., Shanmugapriya, D., Asha, S. (2022). A Framework for Improving the Accuracy with Different Sampling Techniques for Detection of Malicious Insider Threat in Cloud. In: Uddin, M.S., Jamwal, P.K., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-0332-8_36
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DOI: https://doi.org/10.1007/978-981-19-0332-8_36
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