Multiclassification Analysis of Volumetric, Protocol, and Application Layer DDoS Attacks

In today's digital landscape, Distributed Denial of Service (DDoS) attacks represent a constantly evolving and significant threat, interrupting online services via many attack vectors. These attacks universally aim to render websites and services inoperable. Developing effective detection strategies is imperative as DDoS attacks become more frequent, varied, and destructive. This paper introduces an in-depth multiclassification analysis of DDoS attacks, categorizing them into Volumetric, Protocol, and Application Layer attacks. Utilizing the extensive CICDDoS2019 dataset, which includes eleven specific DDoS attack variations, we systematically classify these variations. Our analysis employs six diverse Machine Learning (ML) models: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), and Extreme Gradient Boosting (XGB). We aim to identify the most effective model for predicting DDoS traffic across each attack category and to ascertain the model with superior overall performance. The findings of this study provide valuable insights into the effectiveness of various ML techniques in countering DDoS attacks, thereby contributing to the fortification of digital infrastructures against this pervasive threat.