Open Access

ARTICLE

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

S. Hariprasad^1,*, 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:

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

Received 20 August 2021; Accepted 21 December 2021; Issue published 25 May 2022

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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Keywords

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