Analysis of Various Machine Learning Approach to Detect Anomaly from Network Traffic

Although conventional network security measures have been effective up until now, machine learning techniques are a strong contender in the present network environment due to their flexibility. In this study, we evaluate how well the latter can identify security issues in a corporative setting Network. In order to do so, we configure and contrast a number of models to determine which one best our demands. In addition, we spread the computational load and storage to support large quantities of data. Our model-building methods, Random Forest and Naive Bayes.