About this document
DDoS Detection via Machine Learning Techniques by Edi Suwandi is a document available to read on EtoBox.
This document summarizes research on using machine learning techniques for feature selection to detect DDoS attacks. It discusses prior research that used techniques like neural networks, naive Bayes, random forests, KNN, and support vector machines (SVM) to classify datasets with different percentages of training and testing data. The random forest technique achieved the highest accuracy of 98.7% for detecting DDoS attacks. Prior studies also examined using statistical analysis, CUSUM algorithms, and n-gra
- Author
- Edi Suwandi
- Language
- EN