Enhancing insider threat detection in cloud environments through ensemble learning

M. Vanitha; M. Navya Patel; K. Madhumitha; J. Sathvika

Author(s)

M. Vanitha Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad, Telangana, India

M. Navya Patel Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad, Telangana, India

K. Madhumitha Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad, Telangana, India

J. Sathvika Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad, Telangana, India

Abstract

Cloud computing has drastically transformed data storage and accessibility, but it has also heightened concerns about insider threats-where employees or privileged users misuse their access to sensitive information. According to the 2022 Cloud Security Report, insider attacks account for approximately 35% of global cloud data breaches, underscoring the urgency of addressing this threat vector. Traditional methods-such as rule-based systems, manual audits, and access log reviews-are not only reactive and labor-intensive but also prone to oversight and human error. These limitations become especially pronounced in large-scale cloud environments, where immense data volumes can overwhelm conventional security measures and delay or obscure detection. In response, this research advocates for a machine learning-driven solution, specifically harnessing ensemble learning techniques. By incorporating algorithms like Random Forest, AdaBoost, and CatBoost, the proposed system can process vast amounts of cloud logs and user activities more efficiently, enabling realtime anomaly detection. This approach significantly reduces false positives, enhances response times, and ultimately strengthens overall security posture against insider threats. Through integrated and automated analysis, ensemble learning models offer a proactive defense mechanism, ensuring greater integrity, confidentiality, and continuity of cloud-based operations.

Publication Details

Page(s) 638-647

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 05, Year: 2024

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Keywords

Cloud Computing, Random Forest, Cloud Security, Insider threats, cloud environments, ensemble learning

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