A distributed framework for distributed denial-of-service attack detection in internet of things environments using deep learning Online publication date: Mon, 29-Apr-2024
by Wawire Amisi Silas; Lawrence Nderu; Dalton Ndirangu
International Journal of Web Engineering and Technology (IJWET), Vol. 19, No. 1, 2024
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Web Engineering and Technology (IJWET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook