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

Ullah, Safi and Jawad, Jawad and Khan, Muazzam and Alkhammash, Eman and Hadjouni, Myriam and Ghadi, Yazeed Yasin and Saeed, Faisal and Pitropakis, Nikolaos (2022) A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering. Sensors, 22 (10). ISSN 1424-8220

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Abstract

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.

Item Type: Article
Identification Number: https://doi.org/10.3390/s22103607
Dates:
DateEvent
6 May 2022Accepted
10 May 2022Published Online
Uncontrolled Keywords: convolution neural network; cybersecurity; deep learning; Internet of Things; intrusion detection
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Faisal Saeed
Date Deposited: 13 May 2022 15:27
Last Modified: 13 May 2022 15:27
URI: http://www.open-access.bcu.ac.uk/id/eprint/13222

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