Title: A distributed framework for distributed denial-of-service attack detection in internet of things environments using deep learning

Authors: Wawire Amisi Silas; Lawrence Nderu; Dalton Ndirangu

Addresses: Jomo Kenyatta University of Agriculture and Technology, United States International University Africa, USIU Road, Off Thika Road, Exit 7, Kenya ' Jomo Kenyatta University of Agriculture and Technology, United States International University Africa, USIU Road, Off Thika Road, Exit 7, Kenya ' Jomo Kenyatta University of Agriculture and Technology, United States International University Africa, USIU Road, Off Thika Road, Exit 7, Kenya

Abstract: Internet of things (IoT) networks dominate industries, homes, organisations, and other aspects of life owing to their automation capabilities. However, IoT networks are vulnerable to attacks, especially distributed denial-of-service (DDoS) attacks, as they tend to have low computational capabilities and are highly diverse. While current research shows the potential of utilising deep learning methods to detect DDoS attacks, there is a lack of a framework that can be used to deploy an effective deep learning algorithm to detect DDoS attacks in heterogeneous IoT environments. Accordingly, this paper developed a DDoS detection framework based on the CNN-BiLSTM model, which can be deployed in a distributed network and includes adequate pre-processing. Simulations were also done to demonstrate the application of the framework and its effectiveness.

Keywords: machine learning; artificial intelligence; internet of things; IoT; deep learning; convolutional neural networks; CNNs; BiLSTM; distributed denial-of-service; DDoS.

DOI: 10.1504/IJWET.2024.138107

International Journal of Web Engineering and Technology, 2024 Vol.19 No.1, pp.67 - 87

Received: 08 Sep 2023
Accepted: 30 Nov 2023
Published online: 29 Apr 2024 *

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