Evaluation of Naive Bayes, Random Forest and Stochastic Gradient Boosting Algorithm on DDoS Attack Detection

Abstract

The Internet has given unlimited access to every user through the network used. Vulnerabilities in a network can also be caused by increasing knowledge about hacking and cracking. This is the reason why information and network security are so important. The dataset used in this study uses a dataset from CIC (Canadian Institute Cybersecurity), which covers 7 different attack scenarios, including brute-force, heartbleed, botnet, dos, DDoS, web attacks, and network infiltration from within. The existing attack documents will be extracted. Feature extraction is a process to find the feature values contained in documents for the text mining process. Based on this explanation, this DDoS attack will generate a log where the attack log will be processed and processed into a CSV file for the classification process using Naive Bayes, Random Forest, and Stochastic Gradient Boosting. In this study, researchers used the Naive Bayes, Random Forest, and Stochastic Gradient Boosting algorithms to generate a classification comparison of DDoS attack data so that researchers can find out which algorithm is the best in generating classifications for DDoS attack cases. The results of this study can be concluded that the average accuracy generated by Naive Bayes is 82.45%, the average accuracy generated by the Random Forest algorithm is 99.78%, and the average accuracy generated by the Stochastic Gradient Boosting algorithm is 100%, so that the SGB algorithm is better than Naive Bayes and Random Forest algorithms in classifying DDoS attacks.