A Comparative Analysis of Machine Learning Models for DDoS Detection in IoT Networks

TL;DR

This paper compares various machine learning models for detecting DDoS attacks in IoT networks, highlighting their strengths, weaknesses, and suitability for real-time security applications.

Contribution

It provides a comprehensive evaluation of multiple ML models for DDoS detection in IoT, identifying the most effective approaches for dynamic network environments.

Findings

XGBoost achieved the highest accuracy among models

Naïve Bayes demonstrated fast detection with moderate accuracy

K-Nearest Neighbours showed balanced performance across metrics

Abstract

This paper presents the detection of DDoS attacks in IoT networks using machine learning models. Their rapid growth has made them highly susceptible to various forms of cyberattacks, many of whose security procedures are implemented in an irregular manner. It evaluates the efficacy of different machine learning models, such as XGBoost, K-Nearest Neighbours, Stochastic Gradient Descent, and Na\"ive Bayes, in detecting DDoS attacks from normal network traffic. Each model has been explained on several performance metrics, such as accuracy, precision, recall, and F1-score to understand the suitability of each model in real-time detection and response against DDoS threats. This comparative analysis will, therefore, enumerate the unique strengths and weaknesses of each model with respect to the IoT environments that are dynamic and hence moving in nature. The effectiveness of these models is analyzed, showing how machine learning can greatly enhance IoT security frameworks, offering adaptive, efficient, and reliable DDoS detection capabilities. These findings have shown the potential of machine learning in addressing the pressing need for robust IoT security solutions that can mitigate modern cyber threats and assure network integrity.