REAL-TIME INSIDER THREAT DETECTION IN CLOUD PLATFORMS THROUGH ENSEMBLE LEARNING AND USER BEHAVIOR ANALYTICS

Abstract

Cloud computing has revolutionized how businesses and individuals store and access data, but it has also introduced significant vulnerabilities—particularly insider threats. These threats occur when employees or privileged users misuse their access to sensitive information. According to the 2022 Cloud Security Report, insider attacks account for approximately 35% of all cloud data breaches worldwide. Detecting insider threats in cloud environments is therefore critical to maintaining data integrity, confidentiality, and business continuity. Ensemble learning models provide a robust solution to counter these threats by improving detection accuracy. Traditional approaches—such as rule-based systems, manual audits, and log analysis by security teams—are reactive, time-consuming, and prone to human error. These methods are increasingly ineffective in large-scale cloud infrastructures, where the massive volume of data often leads to delayed or missed detection of malicious activities. The growing number of insider incidents, combined with the limitations of conventional systems, highlights the urgent need for automated and intelligent threat detection solutions. Leveraging machine learning—particularly ensemble models like Random Forest, AdaBoost, and CatBoost—enables early identification of insider threats by analyzing user behavior patterns, detecting anomalies, and flagging potential risks in real time. These models process extensive cloud log data and user activities far more efficiently than manual methods, reducing false positives and strengthening security response capabilities.