A Novel Approach for Bluetooth Mesh Network Detection using Machine Learning and Correlation Analysis Article Swipe
P. Ravi Kiran Varma
,
Krishna Kanth Varma Penmatsa
,
Leela Sai Pavan Kethinedi
,
Dheeraj Sakkuri
,
Rama Krishna Sai Satwik Ramineedi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2024.03.247
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2024.03.247
Bluetooth (BT) mesh networks are used in several ad hoc network communication scenarios, including natural disasters, calamities, war zones, etc. However, there are situations where Bluetooth mesh networks are misused by anti-social elements during riots etc. As of now there no such research article that showcases detection of BT mesh networks using machine learning tools. Therefore, there is a need to build novel methods for detecting the very presence of Bluetooth mesh networks to aid the law enforcement agencies. This paper contributes to building a new dataset for the aid of machine learning tools to detect the presence of Bluetooth mesh networks. The dataset is extracted from a simulated Bluetooth mesh network with 52 BT network traffic attributes, using the Bridgefy application. Further, the dataset is optimized by identifying prime features, using label encoding, one-hot encoding, correlation analysis and evaluated by various Machine Learning (ML) tools. An accuracy of 99.6% is achieved after optimizing the feature set.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2024.03.247
- OA Status
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- References
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4394565306Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.procs.2024.03.247Digital Object Identifier
- Title
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A Novel Approach for Bluetooth Mesh Network Detection using Machine Learning and Correlation AnalysisWork title
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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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P. Ravi Kiran Varma, Krishna Kanth Varma Penmatsa, Leela Sai Pavan Kethinedi, Dheeraj Sakkuri, Rama Krishna Sai Satwik RamineediList of authors in order
- Landing page
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https://doi.org/10.1016/j.procs.2024.03.247Publisher landing page
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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/10.1016/j.procs.2024.03.247Direct OA link when available
- Concepts
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Computer science, Bluetooth, Artificial intelligence, Correlation, Machine learning, Mesh networking, Operating system, Wireless, Geometry, MathematicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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15Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.accuracy | 147 |
| abstract_inverted_index.achieved | 151 |
| abstract_inverted_index.analysis | 137 |
| abstract_inverted_index.building | 83 |
| abstract_inverted_index.elements | 32 |
| abstract_inverted_index.learning | 53, 92 |
| abstract_inverted_index.networks | 3, 27, 50, 72 |
| abstract_inverted_index.presence | 68, 97 |
| abstract_inverted_index.research | 42 |
| abstract_inverted_index.Bluetooth | 0, 25, 70, 99, 109 |
| abstract_inverted_index.agencies. | 78 |
| abstract_inverted_index.detecting | 65 |
| abstract_inverted_index.detection | 46 |
| abstract_inverted_index.encoding, | 133, 135 |
| abstract_inverted_index.evaluated | 139 |
| abstract_inverted_index.extracted | 105 |
| abstract_inverted_index.features, | 130 |
| abstract_inverted_index.including | 13 |
| abstract_inverted_index.networks. | 101 |
| abstract_inverted_index.optimized | 126 |
| abstract_inverted_index.showcases | 45 |
| abstract_inverted_index.simulated | 108 |
| abstract_inverted_index.Therefore, | 55 |
| abstract_inverted_index.disasters, | 15 |
| abstract_inverted_index.optimizing | 153 |
| abstract_inverted_index.scenarios, | 12 |
| abstract_inverted_index.situations | 23 |
| abstract_inverted_index.anti-social | 31 |
| abstract_inverted_index.attributes, | 117 |
| abstract_inverted_index.calamities, | 16 |
| abstract_inverted_index.contributes | 81 |
| abstract_inverted_index.correlation | 136 |
| abstract_inverted_index.enforcement | 77 |
| abstract_inverted_index.identifying | 128 |
| abstract_inverted_index.application. | 121 |
| abstract_inverted_index.communication | 11 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5001517900 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I4400009046 |
| citation_normalized_percentile.value | 0.04861488 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |