Gupta, B B, Chui, Kwok Tai, Gaurav, Akshat, Arya, Varsha and Chaurasia, Priyanka (2023) A Novel Hybrid Convolutional Neural Network- and Gated Recurrent Unit-Based Paradigm for IoT Network Traffic Attack Detection in Smart Cities. Sensors, 23 (21). p. 8686. ISSN 1424-8220
23/21/8686 - Published Version
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Abstract
Internet of Things (IoT) devices within smart cities, require innovative detection methods. This paper addresses this critical challenge by introducing a deep learning-based approach for the detection of network traffic attacks in IoT ecosystems. Leveraging the Kaggle dataset, our model integrates Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to capture both spatial and sequential features in network traffic data. We trained and evaluated our model over ten epochs, achieving an impressive overall accuracy rate of 99%. The classification report reveals the model’s proficiency in distinguishing various attack categories, including ‘Normal’, ‘DoS’ (Denial of Service), ‘Probe’, ‘U2R’ (User to Root), and ‘Sybil’. Additionally, the confusion matrix offers valuable insights into the model’s performance across these attack types. In terms of overall accuracy, our model achieves an impressive accuracy rate of 99% across all attack categories. The weighted- average F1-score is also 99%, showcasing the model’s robust performance in classifying network traffic attacks in IoT devices for smart cities. This advanced architecture exhibits the potential to fortify IoT device security in the complex landscape of smart cities, effectively contributing to the safeguarding of critical infrastructure
Affiliation: | Skyline University College |
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SUC Author(s): | Gupta, B B |
All Author(s): | Gupta, B B, Chui, Kwok Tai, Gaurav, Akshat, Arya, Varsha and Chaurasia, Priyanka |
Item Type: | Article |
Uncontrolled Keywords: | network traffic attacks; IoT; smart cities; deep learning; CNN; GRU |
Subjects: | B Information Technology > BP Internet of Things B Information Technology > BR Deep Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 29 Jan 2024 06:47 |
Last Modified: | 29 Jan 2024 06:47 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/806 |
Publisher URL: | https://doi.org/10.3390/s23218686 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/17524 |
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