Because of the recent exponential rise in attack frequency and sophistication, the proliferation of smart things has created significant cyber security challenges. Even though the tremendous changes cloud computing has brought to the business world, its centralization makes it challenging to use distributed services like security systems. Valuable data breaches might occur due to the high volume of data that moves between businesses and cloud service suppliers, both accidental and malicious. Unlike outsiders, insiders possess privileged and proper access to information and resources. In this work, a machine learning-based system for insider threat detection and classification is proposed and developed a systematic approach to identify various anomalous occurrences that may point to anomalies and security problems associated with privilege escalation. By combining many models, ensemble learning enhances machine learning outcomes and enables greater prediction performance. Multiple studies have been presented regarding detecting irregularities and vulnerabilities in network systems to find security flaws or threats involving privilege escalation. But these studies lack the proper identification of the attacks. This study proposes and evaluates ensembles of Machine learning (ML) techniques in this context. This paper implements machine learning algorithms for the classification of insider attacks. A customized dataset from multiple files of the CERT dataset is used. Four machine learning algorithms, i.e., Random Forest (RF), Adaptive boosting(AdaBoost), Extreme Gradient Boosting(XGBoost), and Light Gradient Boosting Machine(LightGBM), are applied to that dataset and analyzed results. Overall, LightGBM performed best.