Open Access

ARTICLE

An IoT Environment Based Framework for Intelligent Intrusion Detection

Hamza Safwan¹, Zeshan Iqbal¹, Rashid Amin¹, Muhammad Attique Khan², Majed Alhaisoni³, Abdullah Alqahtani⁴, Ye Jin Kim⁵, Byoungchol Chang^6,*

1 Deparment of Computer Science, UET Taxila, Taxila, 47080, Pakistan
2 Department of Computer Science, HITEC University, Taxila, 47080, Pakistan
3 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
4 College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
5 Department of Computer Science, Hanyang University, Seoul, 04763, Korea
6 Center for Computational Social Science, Hanyang University, Seoul, 04763, Korea

* Corresponding Author: Byoungchol Chang. Email:

Computers, Materials & Continua 2023, 75(2), 2365-2381. https://doi.org/10.32604/cmc.2023.033896

Received 30 June 2022; Accepted 07 September 2022; Issue published 31 March 2023

Abstract

Software-defined networking (SDN) represents a paradigm shift in network traffic management. It distinguishes between the data and control planes. APIs are then used to communicate between these planes. The controller is central to the management of an SDN network and is subject to security concerns. This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks. Overfitting, low accuracy, and efficient feature selection is all discussed. We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory (LSTM). In this study, a new dataset based specifically on Software Defined Networks is used in SDN. To obtain the best and most relevant features, a feature selection technique is used. Several experiments have revealed that the proposed solution is a superior method for detecting flow-based anomalies. The performance of our proposed model is also measured in terms of accuracy, recall, and precision. F1 rating and detection time Furthermore, a lightweight model for training is proposed, which selects fewer features while maintaining the model’s performance. Experiments show that the adopted methodology outperforms existing models.

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Keywords

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