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Over the past two decades, Distributed Denial of Service (DDoS) attacks have been responsible for most of the catastrophic failures in the Internet causing a huge amount of disruption of services across all sectors of the economy. Almost every year this attack scores top among all other attacks in terms of the cost to the overall global economy. Machine Learning (ML)based Intrusion Detection Systems (IDSs) heal the global economy with the goal of reducing the prevalence of cyber incidents, such as DDoS. In an ML classification problem, the feature selection process, aka feature engineering, is treated as a mandatory preprocessing phase that potentially reduces the computational complexity by identifying important or relevant features from the original dataset and results in the overall improvement of classification accuracy. In this paper, we propose an ensemble framework for feature selection methods (EnFS …