Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Efficient Identification of DDoS Attacks on Cloud Computing Environment using Machine Learning Techniques

Author : Suzaifa 1 Abdul Khader 2 Sareen Fathima 3

Date of Publication :31st January 2022

Abstract: The internet gives us beneficial data and information for personal, social, and economic growth. The security issue will be the main challenge on the internet. As the cloud technology is more popular, the first advantage of a cloud is flexibly scales to satisfy a variable demand, blew up instantly, blew down when demand moderates– in subsequent. So it requires expanse shielding from a DDoS attacks to challenge interlude effects of a DDoS Attacks. DDoS attacks are the malicious endeavor to interrupt normal traffic in a web property i.e., critical attacks that negotiates the accessibility of the network. On the cloud environment, aiming at efficiently analyzing and detecting the DDoS attacks, we propose four machine learning techniques i.e., the Naive Bayes, Support Vector Machine, K-nearest neighbor, and Random Forest.

Reference :

    • Marwane Zekri, Said El Kafhali, Noureddine Aboutabit, and Youssef Saadi. “DDoS Attack Detection using Machine Learning Techniques in Cloud Computing Environments.” IEEE, 2017.
    • Xiaoyong Yuan, Chuanhuang Li, Xiaolin Li. “DeepDefense: Identifying DDoS Attack via Deep Learning.” IEEE, 2017.
    • Chenxu Wang, Tony T. N. Miu, Xiapu Lu, and Jinhe Wang. “SkyShield: A Sketch-based Defense System Against Application Layer DDoS Attacks.” IEEE, 2017.
    • Bahman Rashidi, Carol Fung, and Elisa Bertino. “A Collaborative DDoS Defence Framework Using Network Function Virtualization.” IEEE,2017.
    • Mohamed Idhammad, Karim Afdel, and Mustapha Belouch. “Detection System of HTTP DDoS Attacks in a Cloud Environment Based on Information Theoretic Entropy and Random Forest.” Security and Communication Network. Hindawi. 2018. 
    • Anteneh Girma, Mosses Garuba, and Rajini Goe. “Advanced Machine Language Approach to Detect DDoS Attack Using DBSCAN Clustering Technology with Entropy.” Information Technology – New Generations, Advances in Intelligent Systems and Computing 558. Springer. 2018.
    • Jieren Cheng, Mengyang Li, Xiangyan Tang, Victor S. Sheng, Yifu Liu, and Wei Guo. “Flow Correlation Degree Optimization Driven Random Forest for Detecting DDoS Attacks in Cloud Computing.” Security and Communication Networks. Hindawi. 2018.
    • S. A. Sokolov, T. B. Iliev and I. S. Stoyanov. “Analysis of Cybersecurity Threats in Cloud Applications Using Deep Learning Techniques.” University of Telecommunications and Posts, Department of Information technology. MIPRO. 2019
    • S. A. Sokolov, T. B. Iliev and I. S. Stoyanov. “Analysis of Cybersecurity Threats in Cloud Applications Using Deep Learning Techniques.” University of Telecommunications and Posts, Department of Information technology. MIPRO. 2019.
    • Kazi Abu Taher, Billal Mohammed Yasin Jisan, Md. Mahbubur Rahman. “Net- work Intrusion Detection using Supervised Machine Learning Technique with Feature Selection.” International Conference on Robotics, Electrical and Signal Processing Techniques. 2019.
    • Abdul Raoof Wani, Q.P. Rana, U. Saxena, Nitin Pandey, “Analysis and Detection of DDoS Attacks on Cloud Computing Environment using Machine Learning Techniques” IEEE, 2019.
    • Hongbin Luo, Member, Zhe Chen, Jiawei Li, Athanasios V. Vasilakos, Senior Member, “Preventing Distributed Denial-of-Service Flooding Attacks with Dynamic Path Identifiers” IEEE, 2017.
    • Rahul Chourasiya, Vaibhav Patel , Anurag Shrivastava, “Classification of Cyber Attack using Machine Learning Technique at Microsoft Azure Cloud”, IRJES, 2018.

Recent Article