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Detection of DDoS Attack in IoT Networks Using Sample Selected RNN-ELM

S. Hariprasad1,*, T. Deepa1, N. Bharathiraja2

1 SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India
2 Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, 600062, Tamil Nadu, India

* Corresponding Author: S. Hariprasad. Email: email

Intelligent Automation & Soft Computing 2022, 34(3), 1425-1440. https://doi.org/10.32604/iasc.2022.022856

Abstract

The Internet of Things (IoT) is a global information and communication technology which aims to connect any type of device to the internet at any time and in any location. Nowadays billions of IoT devices are connected to the world, this leads to easily cause vulnerability to IoT devices. The increasing of users in different IoT-related applications leads to more data attacks is happening in the IoT networks after the fog layer. To detect and reduce the attacks the deep learning model is used. In this article, a hybrid sample selected recurrent neural network-extreme learning machine (hybrid SSRNN-ELM) algorithm that uses recurrent neural network (RNN) as a supervised and extreme learning machine (ELM) classifier as unsupervised. In the proposed algorithm sample selected features are extracting from the original dataset using linear regression with recursive feature extraction (LR-RFE) and sequence forward selector (SFS) then RNN is used to learn the behavior of the important features and at end layer the ELM classifier is used. This hybrid intrusion detection algorithm is placed in between the fog layer and its devices. NSL_KDD benchmark is used for detecting the distributed denial-of-service (DDoS) attack in IoT devices after the fog node. The proposed hybrid SSRNN-ELM model exposes the attacks while testing with enhanced accuracy of up to 99% from NSL-KDD data set. Experimental results outperform by using proposed technique when compared with the existing models.

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APA Style
Hariprasad, S., Deepa, T., Bharathiraja, N. (2022). Detection of ddos attack in iot networks using sample selected RNN-ELM. Intelligent Automation & Soft Computing, 34(3), 1425-1440. https://doi.org/10.32604/iasc.2022.022856
Vancouver Style
Hariprasad S, Deepa T, Bharathiraja N. Detection of ddos attack in iot networks using sample selected RNN-ELM. Intell Automat Soft Comput . 2022;34(3):1425-1440 https://doi.org/10.32604/iasc.2022.022856
IEEE Style
S. Hariprasad, T. Deepa, and N. Bharathiraja "Detection of DDoS Attack in IoT Networks Using Sample Selected RNN-ELM," Intell. Automat. Soft Comput. , vol. 34, no. 3, pp. 1425-1440. 2022. https://doi.org/10.32604/iasc.2022.022856



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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