Open AccessMachine Learning Solutions for Analysis and Detection of DDoS Attacks in Cloud Computing Environment
Author(s) -
Abdul Raoof Wani,
Q. P. Rana,
Nitin Pandey
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b3402.029320
Subject(s) - denial of service attack , computer science , cloud computing , intrusion detection system , support vector machine , naive bayes classifier , decision tree , random forest , computer security , machine learning , artificial intelligence , operating system , the internet
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: kmeans, decision tree, Random Forest, Naïve Bayes, Support Vector Machine and C4.5