Intrusion Detection Framework for the Internet of Things Using a Dense Random Neural Network Article Swipe
Shahid Latif
,
Zil e Huma
,
Sajjad Shaukat Jamal
,
Fawad Ahmed
,
Jawad Ahmad
,
Adnan Zahid
,
Kia Dashtipour
,
Muhammad Umar Aftab
,
Muhammad Ahmad
,
Qammer H. Abbasi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tii.2021.3130248
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tii.2021.3130248
The Internet of Things (IoT) devices, networks, and applications have become an integral part of modern societies. Despite their social, economic, and industrial benefits, these devices and networks are frequently targeted by cybercriminals. Hence, IoT applications and networks demand lightweight, fast and flexible security solutions to overcome these challenges. In this regard, Artificial Intelligence (AI)-based solutions with big data analytics can produce promising results in the field of cybersecurity. This article proposes a lightweight Dense Random Neural Network (DnRaNN) for intrusion detection in the IoT. The proposed scheme is well suited for implementation in resource-constrained IoT networks due to its inherent improved generalization capabilities and distributed nature. The suggested model was evaluated by conducting extensive experiments on a new generation IoT security dataset ToN_IoT. All the experiments were conducted under different hyperparameters and the efficiency of the proposed DnRaNN was evaluated through multiple performance metrics. The findings of the proposed study provide recommendations and insights in binary class and multiclass scenarios. The proposed DnRaNN model attained attack detection accuracy of 99.14% and 99.05% for binary class and multiclass classifications, respectively.
Related Topics
Concepts
Computer science
Hyperparameter
Intrusion detection system
Internet of Things
Artificial neural network
Field (mathematics)
Class (philosophy)
Artificial intelligence
Big data
Generalization
Distributed computing
Binary number
Machine learning
Data mining
Computer security
Mathematics
Pure mathematics
Arithmetic
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tii.2021.3130248
- https://ieeexplore.ieee.org/ielx7/9424/9795344/09626591.pdf
- OA Status
- hybrid
- Cited By
- 104
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3215575541
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3215575541Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tii.2021.3130248Digital Object Identifier
- Title
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Intrusion Detection Framework for the Internet of Things Using a Dense Random Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-11-24Full publication date if available
- Authors
-
Shahid Latif, Zil e Huma, Sajjad Shaukat Jamal, Fawad Ahmed, Jawad Ahmad, Adnan Zahid, Kia Dashtipour, Muhammad Umar Aftab, Muhammad Ahmad, Qammer H. AbbasiList of authors in order
- Landing page
-
https://doi.org/10.1109/tii.2021.3130248Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/9424/9795344/09626591.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/9424/9795344/09626591.pdfDirect OA link when available
- Concepts
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Computer science, Hyperparameter, Intrusion detection system, Internet of Things, Artificial neural network, Field (mathematics), Class (philosophy), Artificial intelligence, Big data, Generalization, Distributed computing, Binary number, Machine learning, Data mining, Computer security, Mathematics, Pure mathematics, Arithmetic, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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104Total citation count in OpenAlex
- Citations by year (recent)
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2025: 24, 2024: 41, 2023: 24, 2022: 15Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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