A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning
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Information Technology, Philadelphia University, Amman 19392, Jordan
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School of Science, Technology and Engineering, University of Granada, 18010 Granada, Spain
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King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan
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School of Computing and Informatics, Al Hussein Technical University, Amman 11831, Jordan
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Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(9), 2987; https://doi.org/10.3390/s21092987
Received: 14 March 2021
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Revised: 11 April 2021
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Accepted: 18 April 2021
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Published: 24 April 2021
(This article belongs to the Section Internet of Things)
The security of IoT networks is an important concern to researchers and business owners, which is taken into careful consideration due to its direct impact on the availability of the services offered by IoT devices and the privacy of the users connected with the network. An intrusion detection system ensures the security of the network and detects malicious activities attacking the network. In this study, a deep multi-layer classification approach for intrusion detection is proposed combining two stages of detection of the existence of an intrusion and the type of intrusion, along with an oversampling technique to ensure better quality of the classification results. Extensive experiments are made for different settings of the first stage and the second stage in addition to two different strategies for the oversampling technique. The experiments show that the best settings of the proposed approach include oversampling by the intrusion type identification label (ITI), 150 neurons for the Single-hidden Layer Feed-forward Neural Network (SLFN), and 2 layers and 150 neurons for LSTM. The results are compared to well-known classification techniques, which shows that the proposed technique outperforms the others in terms of the G-mean having the value of 78% compared to 75% for KNN and less than 50% for the other techniques.
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Keywords:
intrusion detection; classification; neural network; deep learning; oversampling; SMOTE; imbalanced; IoTID20
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MDPI and ACS Style
Qaddoura, R.; M. Al-Zoubi, A.; Faris, H.; Almomani, I. A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning. Sensors 2021, 21, 2987. https://doi.org/10.3390/s21092987
AMA Style
Qaddoura R, M. Al-Zoubi A, Faris H, Almomani I. A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning. Sensors. 2021; 21(9):2987. https://doi.org/10.3390/s21092987
Chicago/Turabian StyleQaddoura, Raneem, Ala’ M. Al-Zoubi, Hossam Faris, and Iman Almomani. 2021. "A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning" Sensors 21, no. 9: 2987. https://doi.org/10.3390/s21092987
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