Title: Prediction of variants of DDoS attacks based on statistical analysis and machine learning algorithms

Authors: Anupama Mishra; Neena Gupta; Brij B. Gupta; Karamjit Bhatia; Mahendra Singh Aswal

Addresses: Department of Computer Science, Gurukula Kangri (Deemed to be University), Haridwar, India ' Department of Computer Science, Gurukula Kangri (Deemed to be University), Haridwar, India ' Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan; Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India; Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India ' Department of Computer Science, Gurukula Kangri (Deemed to be University), Haridwar, India ' Department of Computer Science, Gurukula Kangri (Deemed to be University), Haridwar, India

Abstract: Protecting our servers and machines is vital since they store our data and resources. Attackers use the latest tools and technologies to launch the attack. Among, several cyberattacks, DDoS attacks are the worst. DDoS attackers employ a variety of methods to exploit machines and consume all server resources to block authorised users. Current DDoS detection methods depend on network topology, cannot detect all types of attacks, use outdated or invalid datasets, and require powerful and expensive infrastructure hardware. In our research, we filter non-legitimate traffic and use machine learning classifiers to predict attack types from attack traffic. These two processes together reduce attack volume and identify attack type to provide a comprehensive DDoS protection and enable targeted reaction and mitigation. We used CIC DDoS 2019 data for the experiment. It records MSSQL, PortMap, LDAP, NetBIOS, Syn, UDP, UDPLag, and benign traffic attacks. Experiments yield promising and satisfying results.

Keywords: DDoS; entropy; machine learning; CISDDoS2019.

DOI: 10.1504/IJICA.2024.143395

International Journal of Innovative Computing and Applications, 2024 Vol.15 No.1, pp.14 - 25

Received: 19 Jul 2023
Accepted: 11 Dec 2023
Published online: 17 Dec 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article