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

Alsoufi, Muaadh A. and Siraj, Maheyzah Md and Ghaleb, Fuad A. and Al-Razgan, Muna and Al-Asaly, Mahfoudh Saeed and Alfakih, Taha and Saeed, Faisal (2024) Anomaly-Based Intrusion Detection Model Using Deep Learning for IoT Networks. Computer Modeling in Engineering & Sciences, 141 (1). pp. 823-845. ISSN 1526-1506

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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.

Item Type:	Article
Identification Number:	10.32604/cmes.2024.052112
Dates:	Date Event 7 June 2024 Accepted 20 August 2024 Published Online
Uncontrolled Keywords:	IoT, anomaly intrusion detection, deep learning, sparse autoencoder, convolutional neural network
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-02 - information technology CAH11 - computing > CAH11-01 - computing > CAH11-01-03 - information systems
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Gemma Tonks
Date Deposited:	21 Oct 2024 14:52
Last Modified:	21 Oct 2024 14:52
URI:	https://www.open-access.bcu.ac.uk/id/eprint/15914

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