Development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities Article Swipe
Ankit Kumar
,
Surbhi Bhatia
,
Saroj Kumar Pandey
,
Achyut Shankar
,
Carsten Maple
,
Arwa Mashat
,
Areej Malibari
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s13677-023-00469-9
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s13677-023-00469-9
Cloud computing is the most recent smart city advancement, made possible by the increasing volume of heterogeneous data produced by apps. More storage capacity and processing power are required to process this volume of data. Data analytics is used to examine various datasets, both structured and unstructured. Nonetheless, as the complexity of data in the healthcare and biomedical communities grows, obtaining more precise results from analyses of medical datasets presents a number of challenges. In the cloud environment, big data is abundant, necessitating proper classification that can be effectively divided using machine language. Machine learning is used to investigate algorithms for learning and data prediction. The Cleveland database is frequently used by machine learning researchers. Among the performance metrics used to compare the proposed and existing methodologies are execution time, defect detection rate, and accuracy. In this study, two supervised learning-based classifiers, SVM and Novel KNN, were proposed and used to analyses data from a benchmark database obtained from the UCI repository. Initially, intrusions were detected using the SVM classification method. The proposed study demonstrated how the novel KNN used for distance capacity outperformed previous studies. The accuracy of the results of both approaches is evaluated. The results show that the intrusion detection system (IDS) with a 98.98% accuracy rate produces the best results when using the suggested system.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13677-023-00469-9
- https://journalofcloudcomputing.springeropen.com/counter/pdf/10.1186/s13677-023-00469-9
- OA Status
- gold
- Cited By
- 26
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381326070
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4381326070Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s13677-023-00469-9Digital Object Identifier
- Title
-
Development of a cloud-assisted classification technique for the preservation of secure data storage in smart citiesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-21Full publication date if available
- Authors
-
Ankit Kumar, Surbhi Bhatia, Saroj Kumar Pandey, Achyut Shankar, Carsten Maple, Arwa Mashat, Areej MalibariList of authors in order
- Landing page
-
https://doi.org/10.1186/s13677-023-00469-9Publisher landing page
- PDF URL
-
https://journalofcloudcomputing.springeropen.com/counter/pdf/10.1186/s13677-023-00469-9Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://journalofcloudcomputing.springeropen.com/counter/pdf/10.1186/s13677-023-00469-9Direct OA link when available
- Concepts
-
Computer science, Cloud computing, Benchmark (surveying), Support vector machine, Volume (thermodynamics), Machine learning, Process (computing), Data mining, Big data, Artificial intelligence, Intrusion detection system, Analytics, Operating system, Physics, Geography, Geodesy, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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26Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 13, 2023: 7Per-year citation counts (last 5 years)
- References (count)
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47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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