An intelligent IDS using bagging based fuzzy CNN for secured communication in vehicular networks Article Swipe
M. Vijay Anand
,
S. Muthurajkumar
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-09633-4
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-09633-4
Internet of Vehicles consists of vehicular nodes that communicate with each other for making intelligent transportation systems, where cyber physical attacks are increasing continuously. Intrusion Detection System (IDS) is able to provide a better security solution for minimizing such cyber physical attacks. Many existing IDSs developed using classification algorithms fail to provide the expected intrusion detection accuracy and they exhibit higher false positive rates. Hence, an efficient Feature Selection Algorithm named Weightage and Ranking Based Feature Selection Algorithm and a Bagging based Fuzzy Convolutional Neural Network classification algorithm with Adam optimizer are proposed in this article which are used to identify the attacks more effectively using bagging with fuzzy inference in the deep convolutional neural network classifier. The proposed system was tested using benchmark and network trace datasets and proved that the proposed IDS enhances the detection accuracy and reduces the false positive rate.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-09633-4
- https://www.nature.com/articles/s41598-025-09633-4.pdf
- OA Status
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- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.1038/s41598-025-09633-4Digital Object Identifier
- Title
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An intelligent IDS using bagging based fuzzy CNN for secured communication in vehicular networksWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-07-24Full publication date if available
- Authors
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M. Vijay Anand, S. MuthurajkumarList of authors in order
- Landing page
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https://doi.org/10.1038/s41598-025-09633-4Publisher landing page
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https://www.nature.com/articles/s41598-025-09633-4.pdfDirect link to full text PDF
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-025-09633-4.pdfDirect OA link when available
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Computer science, Fuzzy logic, Artificial intelligence, Data mining, Machine learningTop 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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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.where | 17 |
| abstract_inverted_index.which | 96 |
| abstract_inverted_index.Hence, | 64 |
| abstract_inverted_index.Neural | 84 |
| abstract_inverted_index.System | 26 |
| abstract_inverted_index.better | 33 |
| abstract_inverted_index.higher | 60 |
| abstract_inverted_index.making | 13 |
| abstract_inverted_index.neural | 114 |
| abstract_inverted_index.proved | 129 |
| abstract_inverted_index.rates. | 63 |
| abstract_inverted_index.system | 119 |
| abstract_inverted_index.tested | 121 |
| abstract_inverted_index.Bagging | 80 |
| abstract_inverted_index.Feature | 67, 75 |
| abstract_inverted_index.Network | 85 |
| abstract_inverted_index.Ranking | 73 |
| abstract_inverted_index.article | 95 |
| abstract_inverted_index.attacks | 20, 102 |
| abstract_inverted_index.bagging | 106 |
| abstract_inverted_index.exhibit | 59 |
| abstract_inverted_index.network | 115, 125 |
| abstract_inverted_index.provide | 31, 51 |
| abstract_inverted_index.reduces | 139 |
| abstract_inverted_index.Internet | 0 |
| abstract_inverted_index.Vehicles | 2 |
| abstract_inverted_index.accuracy | 56, 137 |
| abstract_inverted_index.attacks. | 41 |
| abstract_inverted_index.consists | 3 |
| abstract_inverted_index.datasets | 127 |
| abstract_inverted_index.enhances | 134 |
| abstract_inverted_index.existing | 43 |
| abstract_inverted_index.expected | 53 |
| abstract_inverted_index.identify | 100 |
| abstract_inverted_index.physical | 19, 40 |
| abstract_inverted_index.positive | 62, 142 |
| abstract_inverted_index.proposed | 92, 118, 132 |
| abstract_inverted_index.security | 34 |
| abstract_inverted_index.solution | 35 |
| abstract_inverted_index.systems, | 16 |
| abstract_inverted_index.Algorithm | 69, 77 |
| abstract_inverted_index.Detection | 25 |
| abstract_inverted_index.Intrusion | 24 |
| abstract_inverted_index.Selection | 68, 76 |
| abstract_inverted_index.Weightage | 71 |
| abstract_inverted_index.algorithm | 87 |
| abstract_inverted_index.benchmark | 123 |
| abstract_inverted_index.detection | 55, 136 |
| abstract_inverted_index.developed | 45 |
| abstract_inverted_index.efficient | 66 |
| abstract_inverted_index.inference | 109 |
| abstract_inverted_index.intrusion | 54 |
| abstract_inverted_index.optimizer | 90 |
| abstract_inverted_index.vehicular | 5 |
| abstract_inverted_index.algorithms | 48 |
| abstract_inverted_index.increasing | 22 |
| abstract_inverted_index.minimizing | 37 |
| abstract_inverted_index.classifier. | 116 |
| abstract_inverted_index.communicate | 8 |
| abstract_inverted_index.effectively | 104 |
| abstract_inverted_index.intelligent | 14 |
| abstract_inverted_index.Convolutional | 83 |
| abstract_inverted_index.continuously. | 23 |
| abstract_inverted_index.convolutional | 113 |
| abstract_inverted_index.classification | 47, 86 |
| abstract_inverted_index.transportation | 15 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.9135098 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |