ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{talpur:2024:Sensors,

• author = "Fauzia Talpur and Imtiaz Ali Korejo and Aftab Ahmed Chandio and Ali Ghulam and Mir. Sajjad Hussain Talpur",
• title = "{ML}-Based Detection of {DDoS} Attacks Using Evolutionary Algorithms Optimization",
• journal = "Sensors",
• year = "2024",
• volume = "24",
• number = "5",
• pages = "Article No. 1672",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1424-8220",
• URL = "https://www.mdpi.com/1424-8220/24/5/1672",
• DOI = "doi:10.3390/s24051672",
• abstract = "The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) attacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantity and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimisation algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimisation, RF-GA Optimisation, and SVM-GA Optimisation methods, employing Evolutionary Algorithms (EAs) Optimisation with Tree-based Pipelines Optimisation Tool (TPOT)-Genetic Programming. Datasets pertaining to DDoS attacks were used to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimised using EAs, achieving remarkable accuracy scores: 99.99percent with the XGB-GA method, 99.50percent with RF-GA, and 99.99percent with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats.",
• notes = "also known as \cite{s24051672}",

}

Genetic Programming entries for Fauzia Talpur Imtiaz Ali Korejo Aftab Ahmed Chandio Ali Ghulam Mir Sajjad Hussain Talpur

Citations