An Implementation of DDoS detection and mitigation system using SVM Classifier.
Project
January 2020
Machine Learning
Description
In this project, the detection and mitigation system of DDoS attacks was built to minimize DDoS attacks on SDN architecture using an SVM Classifier.
SVM is applied to the machine learning model to classify normal traffic and DDoS attack traffic based on features taken from flow entries.
From the test results, the system has been able to detect DDoS attacks with an average accuracy of 96.83% and an average detection time of 67.80 ms.
