DDoS Attacks in Traffic Flow Streams Using Ensemble Classifiers

Abstract

The failure of internet networking systems, which can happen in various methods, may negatively impact contemporary information and communication technologies. In these circumstances, DDoS attacks have targeted a growing number of organizations. These attacks use a deluge of demands for computation and communication resources to order a service unavailable to genuine users. Distributed denial-of-service (DDoS) attacks must be prevented on vital resources. This manuscript's most important new development is the DDoS attack defence ensemble classifier model. The suggested model uses these specifications to enable a drift detection feature and includes defining streaming properties for service requests. Additionally, an ensemble classifier is used to detect changes in the pattern of service request traffic. Statistical metrics like true negative rate, positive predictive value, and accuracy were used to test and analyze the service request stream synthesis results. Comparing the model to other benchmark models discussed in recent academic works would have been another tactic that could have been used to increase the model's importance.