DDoS Attack Prediction Using Decision Tree and Random Forest Algorithms

The most common network attacks are Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks which causes packet loss by delaying the exchange of information, thereby altering the data packets sent through networks which affect the integrity and reliability of the data. Over time, various machine learning models have been identified and presented by researchers to predict and prevent DoS and DDoS attacks. Many researchers have proposed and used different machine learning techniques to predict DoS and DDoS attacks, however, there is still a need for improvement in the accuracy of prediction and more evaluation of these algorithms and a need for more algorithms to be explored. Hence, this paper improves on existing works by re-evaluating and comparing the accuracy between Decision Tree and Random Forest Algorithms in predicting DDoS attacks. The results of the paper show that Random Forest (RF) Regression model is the best-fit model for the cleaned DDoS SDN dataset used because it is more accurate as it has a lesser mean squared error of 0.21091041940417007 for the test data compared to the mean squared error value of Decision Tree Regression (DTR) Model. Hence, the paper concludes that the RF model is the best-fit model to be used in predicting DDoS attacks. However, the paper proposes that more machine learning algorithms should be explored, implemented, and re-evaluated in detecting DDoS attacks.