A DDoS Attack Detection using PCA Dimensionality Reduction and Support Vector Machine

Distributed denial-of-service attack (DDoS) is one of the most frequently occurring network attacks. Because of rapid growth in the communication and computer technology, the DDoS attacks became severe. So, it is essential to research the detection of a DDoS attack. There are different modes of DDoS attacks because of which a single method cannot provide good security. To overcome this, a DDoS attack detection technique is presented in this paper using machine learning algorithm. The proposed method has two phases, dimensionality reduction and model training for attack detection. The first phase identifies important components from the large proportion of the internet data. These extracted components are used as machine learning’s input features in the phase of model detection. Support Vector Machine (SVM) algorithm is used to train the features and learn the model. The experimental results shows that the proposed method detects DDoS attacks with good accuracy