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

被引：0

作者：

Silas W.A. ^{[1
]}

Nderu L. ^{[1
]}

Ndirangu D. ^{[1
]}

机构：

[1] Jomo Kenyatta University of Agriculture and Technology, United States International University Africa, USIU Road, Off Thika Road

来源：

International Journal of Web Engineering and Technology | 2024年 / 19卷 / 01期

关键词：

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

D O I：

10.1504/IJWET.2024.138107

中图分类号：

学科分类号：

摘要：

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. Copyright © 2024 Inderscience Enterprises Ltd.

引用

页码：67 / 87

页数：20

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