Machine Learning Solutions for Analysis and Detection of DDoS Attacks in Cloud Computing Environment
Abdul Raoof Wani1, Q. P. Rana2, Nitin Pandey3
1Abdul Raoof Wani*, Scholar, Amity Institute of Information Technology, Noida, Uttar Pradesh, India.
2Q. P. Rana, Assistant Professor, Jamia Hamdard University New Delhi, India.
3Nitin Pandey, Assistant Professor, University of Uttar Pradesh, Amity Institute of Information Technology, Noida, Uttar Pradesh, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2205-2209 | Volume-9 Issue-3, February 2020. | Retrieval Number: B3402129219/2020©BEIESP | DOI: 10.35940/ijeat.B3402.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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: kmeans, decision tree, Random Forest, Naïve Bayes, Support Vector Machine and C4.5
Keywords: Machine learning, K-Means, Decision Tree, C4.5, SVM, Naïve Bayes, Random Forest, DDoS, Cloud Computing.