Detecting DDoS Attack
G. Megala, S. Prabu, B. C. Liyanapathirana · Advances in computational intelligence and robotics book series · 2020
The major network security problems faced by many internet users is the DDoS (distributed denial of service) attack. This attack makes the service inaccessible by exhausting the network and resources with high repudiation and economic loss. It denies the network services to the potential users. To detect this DDoS attack accurately in the network, random forest classifier which is a machine learning based classifier is used. The experimental results are compared with naïve Bayes classifier and KNN classifier showing that random forest produces high accuracy results in classification. Application of machine learning, detecting DDoS attacks is modeled based on the supervised learning algorithm to produce best outcome with high accuracy of training algorithm on network dataset.