  DDoS attacks, also known as distributed denial of service (DDoS) attacks,
have emerged as one of the most serious and fastest-growing threats on the
Internet. Denial-of-service (DDoS) attacks are an example of cyber attacks that
target a specific system or network in an attempt to render it inaccessible or
unusable for a period of time. As a result, improving the detection of diverse
types of DDoS cyber threats with better algorithms and higher accuracy while
keeping the computational cost under control has become the most significant
component of detecting DDoS cyber threats. In order to properly defend the
targeted network or system, it is critical to first determine the sort of DDoS
assault that has been launched against it. A number of ensemble classification
techniques are presented in this paper, which combines the performance of
various algorithms. They are then compared to existing Machine Learning
Algorithms in terms of their effectiveness in detecting different types of DDoS
attacks using accuracy, F1 scores, and ROC curves. The results show high
accuracy and good performance.
