Frameworks for Managing Insider Threats Using Behavioral Analytics and Machine Learning in Hybrid Clouds

This research provides an integrated framework for handling the insider threat problem for hybrid cloud environments utilizing the behavioral analytics and machine learning. The proposed system incorporates aspects of user activity monitoring, anomaly detection and automatic threat response in a multi-layered architecture. For the establishment of behavioral baselines, both unsupervised learning models such as Isolation Forest and Autoencoders and supervised classifiers such as Random Forest and XGBoost are used. The framework utilizes cloud-native monitoring tools that possesses the ability to aggregate data securely across on premise and public cloud infrastructures to facilitate scalability and real-time threat detection. A synthetic insider threat dataset modeling hybrid cloud behavior was used in experiments. The model resulted in overall accuracy of 93.6%, precision 92.1% and recall of 91.5%, F1-score of 91.8%. The MSE of 0.009, MAE of 0.062 in each event while proving the efficiency of the system. This framework provides a robust, scalable & adaptive approach to reduce insider threats in complex distributed cloud ecosystem.