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An efficient machine learning (ML) ensemble technique for categorizing Intrusion Detection (ID) is proposed in this study. The tuning of the ML model’s parameters is a critical topic since it can improve detection quality. Another area where quality might be enhanced is pre-processing. Corrections to the training dataset can help with class identification, especially for unusual attacks like R2L (Root to Local attacks), U2R (User to Root attack). When compared to existing methodologies, the proposed methodology has a number of advantages, such as (1) it proposes two methods for classifying intrusions on the two most widely used datasets using ML models. (2) The KDD Cup99 and NSL-KDD datasets are rebalanced through data augmentation. (3) Provides a 3 steps approach for improving detection of intrusion utilizing Multi Layer Perceptron (MLP) in a cascaded structure. (4) To classify each class using a …