Comparison of Data Cleansing Methods for Network DDoS Attacks Mitigation - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Comparison of Data Cleansing Methods for Network DDoS Attacks Mitigation

Résumé

A Distributed Denial of Service (DDoS) attack is a malicious attempt to disrupt the normal traffic of a targeted server, service, or network by overwhelming it with a flood of requests from multiple compromised internet-connected devices, such as distributed servers, personal computers, and Internet of Things devices. One of the methods used to defend against DDoS attacks is traffic redirection to a Scrubbing Center (SC) for further inspection and mitigation. In this research, we present a novel scrubbing method that employs machine learning models to detect DDoS attacks. We propose using three machine learning algorithms, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), and combine them with three feature selection techniques, Analysis of Variance (ANOVA), Principal Component Analysis (PCA), and Kendall's Rank Correlation. Our results indicate that a combination of Kendall's Rank Correlation as a feature selector with SVM, XGBoost, and Random Forest models achieved a high F1 score.
Fichier non déposé

Dates et versions

hal-04370537 , version 1 (03-01-2024)

Identifiants

Citer

Adonis Jamal, Ali El Attar, Fadlallah Chbib, Rida Khatoun. Comparison of Data Cleansing Methods for Network DDoS Attacks Mitigation. 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), Jul 2023, Rome, Italy. pp.459-464, ⟨10.1109/CoDIT58514.2023.10284093⟩. ⟨hal-04370537⟩
19 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More