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Security is always a major concern in today's world. Due to the prevalent techniques like Internet of Things (IoT), Fog/Edge computing and the vast use of social networking, there is a significant increase in the generation of network traffic data. For this reason, proper and fast mechanisms are needed to monitorvariety of data to fight against the vulnerabilities and threats those may occur in the system. In the present article, a machine learning based Intrusion Detection Scheme (IDS) is being proposed. This system can monitor and analyze the incoming network traffic whether is normal. UNSW-NB 15 dataset is used to validate the machine learning model which is powered by boosting algorithm.Three of the boosting algorithms such as Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost) and Gradient Boosting Classifier (GBC) are trained over the six baseline models such as Support Vector Machine …