A Fast Internet of Things DDoS Attack Detection Method Using Deep Feedforward Networks

Authors

  • Vahide Babaiyan * Computer Engineering and IT Department, Faculty of Electrical and Computer Engineering, Shiraz University of Technology, Shiraz, Iran.
  • Omid Bushehrian Computer Engineering and IT Department, Faculty of Electrical and Computer Engineering, Shiraz University of Technology, Shiraz, Iran. https://orcid.org/0000-0001-9912-4326
  • Reza Javidan Computer Engineering and IT Department, Faculty of Electrical and Computer Engineering, Shiraz University of Technology, Shiraz, Iran. https://orcid.org/0000-0002-7788-6597

https://doi.org/10.48314/apem.v2i2.33

Abstract

The increasing use of Internet of Things (IoT) devices has led to a surge in data traffic, which can be vulnerable to intentional denial-of-service (DoS) attacks that disrupt the intended Quality of Service (QoS). This paper presents a deep learning-based approach using Feedforward Neural Networks (FNNs) to detect Distributed Denial-of-Service (DDoS) attacks in IoT networks. We evaluated the performance of this approach on the IoT-23 dataset, which included captures of both malware-infected and benign IoT traffic. We conducted a comparative analysis between the FNN approach and three commonly used Machine Learning (ML) models, namely, Support Vector Machines (SVM), Random Forests (RFs), and Gradient Boosting (GRB). Our findings demonstrate that all methods achieve similar levels of accuracy. However, the FNN model distinguishes itself with significantly higher precision than the other models. Furthermore, our analysis revealed that FNN exhibits the shortest learning time among the considered models.

Keywords:

Internet of things, Traffic classification, Supervised learning, Distributed denial-of-service attack, Internet of things-23

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Published

2025-05-13

Issue

Vol. 2 No. 2 (2025): Annals of Process Engineering and Management

Section

Articles

How to Cite

A Fast Internet of Things DDoS Attack Detection Method Using Deep Feedforward Networks. (2025). Annals of Process Engineering and Management, 2(2), 101-111. https://doi.org/10.48314/apem.v2i2.33

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