Open Access

ARTICLE

Detection of Real-Time Distributed Denial-of-Service (DDoS) Attacks on Internet of Things (IoT) Networks Using Machine Learning Algorithms

by Zaed Mahdi^1,*, Nada Abdalhussien², Naba Mahmood¹, Rana Zaki^3,*

1 Information Technology Center, University of Technology, Baghdad, 00964, Iraq
2 Continuous Education Center, University of Technology, Baghdad, 00964, Iraq
3 Computer Science Department, University of Technology, Baghdad, 00964, Iraq

* Corresponding Authors: Zaed Mahdi. Email: ; Rana Zaki. Email:

Computers, Materials & Continua 2024, 80(2), 2139-2159. https://doi.org/10.32604/cmc.2024.053542

Received 03 May 2024; Accepted 20 June 2024; Issue published 15 August 2024

Abstract

The primary concern of modern technology is cyber attacks targeting the Internet of Things. As it is one of the most widely used networks today and vulnerable to attacks. Real-time threats pose with modern cyber attacks that pose a great danger to the Internet of Things (IoT) networks, as devices can be monitored or service isolated from them and affect users in one way or another. Securing Internet of Things networks is an important matter, as it requires the use of modern technologies and methods, and real and up-to-date data to design and train systems to keep pace with the modernity that attackers use to confront these attacks. One of the most common types of attacks against IoT devices is Distributed Denial-of-Service (DDoS) attacks. Our paper makes a unique contribution that differs from existing studies, in that we use recent data that contains real traffic and real attacks on IoT networks. And a hybrid method for selecting relevant features, And also how to choose highly efficient algorithms. What gives the model a high ability to detect distributed denial-of-service attacks. the model proposed is based on a two-stage process: selecting essential features and constructing a detection model using the K-neighbors algorithm with two classifier algorithms (logistic regression and Stochastic Gradient Descent classifier (SGD), combining these classifiers through ensemble machine learning (stacking), and optimizing parameters through Grid Search-CV to enhance system accuracy. Experiments were conducted to evaluate the effectiveness of the proposed model using the CIC-IoT2023 and CIC-DDoS2019 datasets. Performance evaluation demonstrated the potential of our model in robust intrusion detection in IoT networks, achieving an accuracy of 99.965% and a detection time of 0.20 s for the CIC-IoT2023 dataset, and 99.968% accuracy with a detection time of 0.23 s for the CIC-DDoS 2019 dataset. Furthermore, a comparative analysis with recent related works highlighted the superiority of our methodology in intrusion detection, showing improvements in accuracy, recall, and detection time.

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Keywords

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