Towards Development of Machine Learning Framework for Enhancing Security in Internet of Things Article Swipe
Mutyalaiah Paricherla
,
Babu Sallagundla
,
Khongdet Phasinam
,
Harikumar Pallathadka
,
Abu Sarwar Zamani
,
Vipul Narayan
,
Surendra Kumar Shukla
,
Hussien Sobahi Mohammed
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/4477507
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/4477507
An IoT system is a smart network that connects all items to the Internet and exchanges data using Internet Engineering Task Force established protocols. As a consequence, everything is instantly accessible from any place and at any time. The Internet of Things (IoT) network is built on the backbone of tiny sensors embedded in common objects. There is no need for human intervention in the interactions of IoT devices. The Internet of Things (IoT) security risk cannot be ignored. Untrusted networks, such as the Internet, are utilized to provide remote access to IoT devices. As a result, IoT systems are susceptible to a broad range of harmful activities, including cyberattacks. If security problems are not addressed, critical information may be hacked at any time. This article describes a feature selection and machine learning-based paradigm for improving security in the Internet of Things. Because network data are inherently abundant, it must be reduced in size before processing. Dimension reduction is the process of constructing a subset of an original data collection that removes superfluous content from the essential data set. Dimension reduction is a data mining approach. To minimize the number of dimensions in a dataset, linear discriminant analysis (LDA) is used. Following that, the data set with fewer dimensions is put into machine learning predictors as a training set. The effectiveness of machine learning approaches has been assessed using a range of criteria.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/4477507
- https://downloads.hindawi.com/journals/scn/2022/4477507.pdf
- OA Status
- hybrid
- Cited By
- 31
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280625219
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Raw OpenAlex JSON
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https://openalex.org/W4280625219Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2022/4477507Digital Object Identifier
- Title
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Towards Development of Machine Learning Framework for Enhancing Security in Internet of ThingsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-05-17Full publication date if available
- Authors
-
Mutyalaiah Paricherla, Babu Sallagundla, Khongdet Phasinam, Harikumar Pallathadka, Abu Sarwar Zamani, Vipul Narayan, Surendra Kumar Shukla, Hussien Sobahi MohammedList of authors in order
- Landing page
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https://doi.org/10.1155/2022/4477507Publisher landing page
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https://downloads.hindawi.com/journals/scn/2022/4477507.pdfDirect link to full text PDF
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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/scn/2022/4477507.pdfDirect OA link when available
- Concepts
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Computer science, The Internet, Linear discriminant analysis, Internet of Things, Dimension (graph theory), Artificial intelligence, Machine learning, Set (abstract data type), Computer security, Dimensionality reduction, Web of Things, World Wide Web, Pure mathematics, Programming language, MathematicsTop concepts (fields/topics) attached by OpenAlex
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31Total citation count in OpenAlex
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2025: 12, 2024: 7, 2023: 11, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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