Utilizing Random Forest for DDoS Attack Detection

Abstract

The “Distributed Denial of Service” (DDoS) attack represents one of the most common forms of cyber assaults. Top of FormThe goal of DDoS is to overwhelm the server machine with an overwhelming number of data packets. This causes the bulk of the network bandwidth and server resources to be used leading to a Distributed denial-of-service problem.  In this paper, we employed a random forest classifier for detecting the DDoS attack. This leads to an improvement in accuracy as well as a reduction in the amount of processing overhead required. Utilizing the CICDDOS2019 dataset, our experimental results showcased an impressive accuracy rate of 99.81%.