Open Access

ARTICLE

Anomaly-Based Intrusion Detection Model Using Deep Learning for IoT Networks

by Muaadh A. Alsoufi^1,*, Maheyzah Md Siraj¹, Fuad A. Ghaleb², Muna Al-Razgan³, Mahfoudh Saeed Al-Asaly³, Taha Alfakih³, Faisal Saeed²

1 Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, 81310, Malaysia
2 DAAI Research Group, College of Computing and Digital Technology, Birmingham City University, Birmingham, B4 7XG, UK
3 Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11345, Saudi Arabia

* Corresponding Author: Muaadh A. Alsoufi. Email:

(This article belongs to the Special Issue: Emerging Technologies in Information Security )

Computer Modeling in Engineering & Sciences 2024, 141(1), 823-845. https://doi.org/10.32604/cmes.2024.052112

Received 23 March 2024; Accepted 07 June 2024; Issue published 20 August 2024

Abstract

The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks exposes them to various security threats, including anomaly intrusion attacks. In addition, IoT devices generate a high volume of unstructured data. Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks, such as resource constraints and heterogeneous data sources. Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency. Across numerous research domains, deep learning techniques have demonstrated their capability to precisely detect anomalies. This study designs and enhances a novel anomaly-based intrusion detection system (AIDS) for IoT networks. Firstly, a Sparse Autoencoder (SAE) is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed error. Secondly, the Convolutional Neural Network (CNN) technique is employed to create a binary classification approach. The proposed SAE-CNN approach is validated using the Bot-IoT dataset. The proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%, precision of 99.9%, recall of 100%, F1 of 99.9%, False Positive Rate (FPR) of 0.0003, and True Positive Rate (TPR) of 0.9992. In addition, alternative metrics, such as training and testing durations, indicated that SAE-CNN performs better.

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Keywords

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