MUS Model: A Deep Learning-Based Architecture for IoT Intrusion Detection Article Swipe
Yu Yan
,
Yu Yang
,
Fang Shen
,
Minna Gao
,
Yiding Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2024.051685
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2024.051685
In the face of the effective popularity of the Internet of Things (IoT), but the frequent occurrence of cybersecurity incidents, various cybersecurity protection means have been proposed and applied. Among them, Intrusion Detection System (IDS) has been proven to be stable and efficient. However, traditional intrusion detection methods have shortcomings such as low detection accuracy and inability to effectively identify malicious attacks. To address the above problems, this paper fully considers the superiority of deep learning models in processing high-dimensional data, and reasonable data type conversion methods can extract deep features and detect classification using advanced computer vision techniques to improve classification accuracy. The Markov Transform Field (MTF) method is used to convert 1D network traffic data into 2D images, and then the converted 2D images are filtered by Unsharp Masking to enhance the image details by sharpening; to further improve the accuracy of data classification and detection, unlike using the existing high-performance baseline image classification models, a soft-voting integrated model, which integrates three deep learning models, MobileNet, VGGNet and ResNet, to finally obtain an effective IoT intrusion detection architecture: the MUS model. Four types of experiments are conducted on the publicly available intrusion detection dataset CICIDS2018 and the IoT network traffic dataset N_BaIoT, and the results demonstrate that the accuracy of attack traffic detection is greatly improved, which is not only applicable to the IoT intrusion detection environment, but also to different types of attacks and different network environments, which confirms the effectiveness of the work done.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2024.051685
- OA Status
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https://openalex.org/W4400260187Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32604/cmc.2024.051685Digital Object Identifier
- Title
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MUS Model: A Deep Learning-Based Architecture for IoT Intrusion DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Yu Yan, Yu Yang, Fang Shen, Minna Gao, Yiding ChenList of authors in order
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https://doi.org/10.32604/cmc.2024.051685Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.32604/cmc.2024.051685Direct OA link when available
- Concepts
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Intrusion detection system, Computer science, Architecture, Deep learning, Artificial intelligence, Internet of Things, Computer architecture, Embedded system, History, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2513263058, https://openalex.org/W1548644355, https://openalex.org/W4312590824, https://openalex.org/W4380479751, https://openalex.org/W4313260004, https://openalex.org/W4389903346, https://openalex.org/W4390670149, https://openalex.org/W6684764998, https://openalex.org/W6676982752, https://openalex.org/W2604320478, https://openalex.org/W6857454671, https://openalex.org/W2100495367, https://openalex.org/W6846651298, https://openalex.org/W4365513113, https://openalex.org/W6845702819, https://openalex.org/W6849230346, https://openalex.org/W6845916227, https://openalex.org/W3168296045, https://openalex.org/W4313036808, https://openalex.org/W2135850590, https://openalex.org/W4308527194, https://openalex.org/W4327518008, https://openalex.org/W4308627432, https://openalex.org/W2545139539, https://openalex.org/W2034037879, https://openalex.org/W1686810756, https://openalex.org/W4295122555, https://openalex.org/W2612445135, https://openalex.org/W2114917462, https://openalex.org/W4297775537, https://openalex.org/W3214373139, https://openalex.org/W4387687418, https://openalex.org/W4319300445, https://openalex.org/W4304688109, https://openalex.org/W4309043412, https://openalex.org/W2166975838, https://openalex.org/W4307874718 |
| referenced_works_count | 37 |
| abstract_inverted_index.a | 157 |
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| abstract_inverted_index.models, | 156, 166 |
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| abstract_inverted_index.However, | 43 |
| abstract_inverted_index.Internet | 9 |
| abstract_inverted_index.N_BaIoT, | 203 |
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| abstract_inverted_index.effective | 5, 175 |
| abstract_inverted_index.improved, | 217 |
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| abstract_inverted_index.malicious | 60 |
| abstract_inverted_index.problems, | 66 |
| abstract_inverted_index.CICIDS2018 | 196 |
| abstract_inverted_index.MobileNet, | 167 |
| abstract_inverted_index.applicable | 222 |
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| abstract_inverted_index.cybersecurity | 18, 21 |
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| abstract_inverted_index.environments, | 239 |
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| abstract_inverted_index.high-performance | 152 |
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| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.63244299 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |