The prediction of DDoS attack by machine learning
Zhongbang Liu, Likun Qian, Shiwen Tang
Abstract
Distributed Denial of Service (DDoS) attacks are malicious cyber-attacks that overwhelm the target server with traffic and cause the online service unavailable. With the wide-scale rollout of Internet of Things (IoT), the DDoS attack has threatened almost all walks of life, including business and government. The accurate prediction and early prevention of DDoS attacks are necessary. In this paper, two machine learning models, the Logistic Regression model and linear Support Vector Machine (SVM), are introduced to make the DDoS attack prediction. Two dimensionality reduction methods, Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) are tried in the data preprocessing. The mean cross-validation accuracy is used to evaluate models’ performance. Experimental results indicate that the linear SVM model performs better than the Logistic Regression model on the DDoS Evaluation Dataset (CIC-DDoS2019). Besides, compared with accuracies of models using RFE, accuracies of models with PCA are higher and more stable. Overfitting is likely to occur in learning models with RFE, according to our observations of losses on the training set and the testing set.