Open Access

ARTICLE

Machine Learning with Dimensionality Reduction for DDoS Attack Detection

by Shaveta Gupta¹, Dinesh Grover², Ahmad Ali AlZubi^3,*, Nimit Sachdeva⁴, Mirza Waqar Baig⁵, Jimmy Singla⁶

1 IK Gujral Punjab Technical University, Jalandhar, 144603, India
2 Department. of Electrical Engineering and Computer Science, Punjab Agriculture University, Ludhiana, 141004, India
3 Department of Computer Science, Community College, King Saud University, Riyadh, 11437, Saudi Arabia
4 Vunsol Private Limited, Mohali, 160055, India
5 Department of Electrical Engineering, FAST National University, CFD Campus, Faisalabad, 44000, Pakistan
6 School of Computer Science and Engineering, Lovely Professional University, Punjab, 144001, India

* Corresponding Author: Ahmad Ali AlZubi. Email:

Computers, Materials & Continua 2022, 72(2), 2665-2682. https://doi.org/10.32604/cmc.2022.025048

Received 09 November 2021; Accepted 29 December 2021; Issue published 29 March 2022

Abstract

With the advancement of internet, there is also a rise in cybercrimes and digital attacks. DDoS (Distributed Denial of Service) attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant threat in the digital environment. These cyber-attacks are generated deliberately and consciously by the hacker to overwhelm the target with heavy traffic that genuine users are unable to use the target resources. As a result, targeted services are inaccessible by the legitimate user. To prevent these attacks, researchers are making use of advanced Machine Learning classifiers which can accurately detect the DDoS attacks. However, the challenge in using these techniques is the limitations on capacity for the volume of data and the required processing time. In this research work, we propose the framework of reducing the dimensions of the data by selecting the most important features which contribute to the predictive accuracy. We show that the ‘lite’ model trained on reduced dataset not only saves the computational power, but also improves the predictive performance. We show that dimensionality reduction can improve both effectiveness (recall) and efficiency (precision) of the model as compared to the model trained on ‘full’ dataset.

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Keywords

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