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

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 unstructure...

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Vydáno v:Computer modeling in engineering & sciences Ročník 141; číslo 1; s. 823 - 845
Hlavní autoři: Alsoufi, Muaadh A., Siraj, Maheyzah Md, Ghaleb, Fuad A., Al-Razgan, Muna, Al-Asaly, Mahfoudh Saeed, Alfakih, Taha, Saeed, Faisal
Médium: Journal Article
Jazyk:angličtina
Vydáno: Henderson Tech Science Press 2024
Témata:
Artificial neural networks
Deep learning
Internet of Things
Intrusion detection systems
Machine learning
Unstructured data
ISSN:1526-1506, 1526-1492, 1526-1506
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Shrnutí: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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ISSN:1526-1506
1526-1492
1526-1506
DOI:10.32604/cmes.2024.052112