IJBTT

DOS-DDOS Attacks Predicting : Performance Comparison of The Main Feature Selection Strategies

DOS-DDOS Attacks Predicting : Performance Comparison of The Main Feature Selection Strategies
	© 2022 by IJETT Journal
	Volume-70 Issue-1
	Year of Publication : 2022
	Authors : Kawtar Bouzoubaa, Youssef Taher, Benayad Nsiri
	DOI :  10.14445/22315381/IJETT-V70I1P235

How to Cite?

Kawtar Bouzoubaa, Youssef Taher, Benayad Nsiri, "DOS-DDOS Attacks Predicting : Performance Comparison of The Main Feature Selection Strategies," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 310-318, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I1P235

Abstract
Today, cyberattacks are one of the largest threats to individuals and societies across the globe. Dealing with the complexity and variety of these threats by using classical solutions such as software/hardware firewalls and antivirus / antimalware becomes insufficient and presents many drawbacks.
To support and improve the efficiency of these traditional solutions, Machine Learning (ML) models can play an increasing role in detecting, preventing or disrupting cyberattacks at the earliest stage (near real-time).
In this context, the focus of the present paper is to analyze and assess how cyberattacks Feature Selection Strategies (FSS) can support the improvement of these ML models performances applied to cybersecurity, especially the case of DOS-DDOS attacks.
By reviewing more than one hundred and three references and using a hierarchical analysis model based on three levels of performance analysis, this paper has compared the performances of four main types of Feature Selection Methods (TFSM) (first analysis level), the main DOS-DDOS Features Selection Sub-Methods (FSSM) used in each TFSM (second analysis level) and DOS-DDOS datasets widely used in ML cybersecurity projects (third analysis level).

Keywords
Cybersecurity; DOS-DDOS Attacks; Machine Learning; Feature Selection Strategies.

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