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A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering

Ullah, Safi; Ahmad, Jawad; Khan, Muazzam A.; Alkhammash, Eman H.; Hadjouni, Myriam; Ghadi, Yazeed Yasin; Saeed, Faisal; Pitropakis, Nikolaos


Safi Ullah

Muazzam A. Khan

Eman H. Alkhammash

Myriam Hadjouni

Yazeed Yasin Ghadi

Faisal Saeed


The Internet of Things (IoT) is a widely used technology in automated network systems across the world. The impact of the IoT on different industries has occurred in recent years. Many IoT nodes collect, store, and process personal data, which is an ideal target for attackers. Several researchers have worked on this problem and have presented many intrusion detection systems (IDSs). The existing system has difficulties in improving performance and identifying subcategories of cyberattacks. This paper proposes a deep-convolutional-neural-network (DCNN)-based IDS. A DCNN consists of two convolutional layers and three fully connected dense layers. The proposed model aims to improve performance and reduce computational power. Experiments were conducted utilizing the IoTID20 dataset. The performance analysis of the proposed model was carried out with several metrics, such as accuracy, precision, recall, and F1-score. A number of optimization techniques were applied to the proposed model in which Adam, AdaMax, and Nadam performance was optimum. In addition, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. All experimental analysis indicates that the accuracy of the proposed approach is high and more robust than existing DL-based algorithms.


Ullah, S., Ahmad, J., Khan, M. A., Alkhammash, E. H., Hadjouni, M., Ghadi, Y. Y., Saeed, F., & Pitropakis, N. (2022). A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering. Sensors, 22(10), Article 3607.

Journal Article Type Article
Acceptance Date May 6, 2022
Online Publication Date May 10, 2022
Publication Date 2022
Deposit Date May 16, 2022
Publicly Available Date May 16, 2022
Journal Sensors
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 22
Issue 10
Article Number 3607
Keywords convolution neural network; cybersecurity; deep learning; Internet of Things; intrusion detection
Public URL


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