Detecting DDoS attacks using a hybrid model - Caychingco, Jedidiah (Animo Repository, 2018)
A Distributed Denial of Service (DDoS) attack can disrupt and damage businesses by preventing legitimate users from accessing its resources. Some estimate their losses to be at 500$ per minute of DDoS. Being able to detect these attacks can allow security analysts to apply the proper techniques in order to mitigate it. Consequently, this study aims to use a two-stage hybrid model in order to detect DDoS attacks. During the first stage, a machine learning algorithm is first used to differentiate normal and attack traffic. If the traffic has been deemed to be part of a DDoS attack, it is passed to the second stage. The second stage involves using another machine learning algorithm in order to determine whether it is part of a low rate or high rate DDoS attack. Each stage will produce a model. In addition, the performance of the hybrid model will be compared against a single model in order to determine which configuration performs better. The models are produced by the following machine learning classifiers: Naive Bayes, Decision Tree, K-Nearest Neighbors, Random Forest, and Support Vector Machines. The models will be evaluated using accuracy, precision, recall, f-score, and the Kappa statistic.