Real-time DDoS flood attack monitoring and detection (RT-AMD) model for cloud computing.

ABSTRACT: In recent years, the advent of cloud computing has transformed the field of computing and information technology. It has been enabling customers to rent virtual resources and take advantage of various on-demand services with the lowest costs. Despite the advantages of cloud computing, it faces several threats; an example is a distributed denial of service (DDoS) attack, which is considered among the most serious. This article presents real-time monitoring and detection of DDoS attacks on the cloud using a machine learning approach. Naïve Bayes, K-nearest neighbor, decision tree, and random forest machine learning classifiers have been selected to build a predictive model named "Real-Time DDoS flood Attack Monitoring and Detection RT-AMD." The DDoS-2020 dataset was constructed with 70,020 r

SUBMITTER: Bamasag O 

PROVIDER: S-EPMC9202629 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature