An Efficient Program to Detect DDoS Attacks using Machine Learning Algorithms

Abstract

This paper investigates the efficacy of machine learning algorithms for the detection of Distributed Denial of Service (DDoS) attacks [4][5]. The study explores different approaches, including Support Vector Machines (SVM), logistic regression, and decision trees, and evaluates their performance using metrics such as accuracy, precision, recall, and F1-score [6]. The results demonstrate the effectiveness of SVM models with polynomial or radial basis function (RBF) kernels, logistic regression models with a polynomial degree of 4, and decision tree models with depths exceeding 10 [7][8]. These algorithm configurations exhibit promising potential in mitigating DDoS attacks and safeguarding network infrastructures [9]. However, limitations such as dataset availability, imbalanced data, and the focus on offline detection warrant further research. Enhancements in these areas can lead to more robust and efficient DDoS detection systems. The findings of this study contribute to the advancement of network security and offer insights for organizations aiming to counter the growing threat of DDoS attacks.