Machine Learning Solutions for Analysis and Detection of DDoS Attacks in Cloud Computing Environment

Abstract

Distributed denial of service is a critical threat that is responsible for halting the normal functionality of services in cloud computing environments. Distributing Denial of Service attacks is categorized in the level of crucial attacks that undermine the network's functionality. These attacks have become sophisticated and continue to grow rapidly, and it has become a challenging task to detect and address these attacks. There is a need for Intelligent Intrusion detection systems that can classify and detect anomalous behavior in network traffic. This research was performed on the cloudstack environment using Tor Hammer as an attacking mechanism, and the Intrusion Detection System produced a new dataset. This analysis incorporates numerous algorithms of machine learning: k-means, decision tree, Random Forest, Naïve Bayes, Support Vector Machine and C4.5

Security of private cloud using machine learning and cryptography

“…Tor Hammer attacking mechanism and IDS has been proposed in [9] and implemented in cloudstack environment to work with a novel dataset. The dataset analyzed with different ML algorithms as the k-means, DT, random forest (RF), Naïve Bayes, SVM, and C4.5.…”