A Novel, Efficient, and Secure Anomaly Detection Technique Using DWU‐ODBN for IoT‐Enabled Multimedia Communication Systems Article Swipe
M. Sathya
,
M. Jeyaselvi
,
Lalitha Krishnasamy
,
Mohammad Mazyad Hazzazi
,
Prashant Kumar Shukla
,
Piyush Kumar Shukla
,
Stephen Jeswinde Nuagah
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/4989410
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/4989410
The Internet of Things (IoT) is enhancing our lives in a variety of structures, which consists of smarter cities, agribusiness, and e‐healthcare, among others. Even though the Internet of Things has many features with the consumer Internet of Things, the open nature of smart devices and their worldwide connection make IoT networks vulnerable to a variety of assaults. Several approaches focused on attack detection in Internet of Things devices, which has the longest calculation times and the lowest accuracy issues. It is proposed in this paper that an attack detection framework for Internet of Things devices, based on the DWU‐ODBN method, be developed to alleviate the existing problems. At the end of the process, the proposed method is used to identify the source of the assault. It comprises steps such as preprocessing, feature extraction, feature selection, and classification to identify the source of the attack. A random oversampler is used to preprocess the input data by dealing with NaN values, categorical features, missing values, and unbalanced datasets before being used to deal with the imbalanced dataset. When the data has been preprocessed, it is then sent to the MAD Median‐KS test method, which is used to extract features from the dataset. To categorize the data into attack and nonattack categories, the features are classified using the dual weight updation‐based optimal deep belief network (DWU‐ODBN) classification technique, which is explained in more detail below. According to the results of the experimental assessment, the proposed approach outperforms existing methods in terms of detecting intrusions and assaults. The proposed work achieves 77 seconds to achieve the attack detection with an accuracy rate of 98.1%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2021/4989410
- https://downloads.hindawi.com/journals/wcmc/2021/4989410.pdf
- OA Status
- hybrid
- Cited By
- 48
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200096008
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200096008Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2021/4989410Digital Object Identifier
- Title
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A Novel, Efficient, and Secure Anomaly Detection Technique Using DWU‐ODBN for IoT‐Enabled Multimedia Communication SystemsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-01-01Full publication date if available
- Authors
-
M. Sathya, M. Jeyaselvi, Lalitha Krishnasamy, Mohammad Mazyad Hazzazi, Prashant Kumar Shukla, Piyush Kumar Shukla, Stephen Jeswinde NuagahList of authors in order
- Landing page
-
https://doi.org/10.1155/2021/4989410Publisher landing page
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https://downloads.hindawi.com/journals/wcmc/2021/4989410.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/wcmc/2021/4989410.pdfDirect OA link when available
- Concepts
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Computer science, Internet of Things, Anomaly detection, Multimedia, Computer network, Embedded system, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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48Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 18, 2023: 5, 2022: 24Per-year citation counts (last 5 years)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | Wireless Communications and Mobile Computing |
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| publication_date | 2021-01-01 |
| publication_year | 2021 |
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