In the current era of Big Data, a high volume of data is being grown in vast and the speed of generating the new data is accelerating quickly. Machine Learning algorithms are used for such large datasets to teach computers how to reply to and act like humans. In machine learning with the help of generalization ability, the increase in the size of the training set increases the scope of testing. In this paper, we analyze the results of the attacks classified using Intrusion Detection System, and the training time of Random Forest algorithm is measured by increasing the size of the KDD dataset in intervals thereby observing the changes in the final evaluation metrics obtained