An Intelligent Approach for Preserving the Privacy and Security of a Smart Home Based on IoT Using LogitBoost Techniques Article Swipe
Asif Rahim
,
Yanru Zhong
,
Tariq Ahmad
,
Umar Islam
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.55463/issn.1674-2974.49.4.39
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.55463/issn.1674-2974.49.4.39
Development and use of IoT devices have grown significantly in recent years. Many departments such as smart homes, smart healthcare, smart sports analysis, and different smart industries use IoT-based devices. In IoT devices, traffic is a very important part. IoT device traffic is distinct from traditional device traffic in various respects. In this study, 41 Internet-of-Things (IoT) devices were used. IoT devices provided 13 network traffic attributes to construct a multiclass classification model. Pre-processing techniques such as Normalization and Scaling of Dataset were used to pre-process the raw data acquired. Features can be extracted from text data using feature engineering algorithms. After stratification, the dataset contains 117,423 feature vectors utilized to develop the classification model further. Multiple performance metrics were used to demonstrate how well LogitBoost algorithms perform in this research. Using ensemble-based hybrid machine learning models to detect network anomalies in this research is an early step in developing an intrusion detection system (IDS). The main objective of this study is to detect attacks and anomalies in an IoT environment in a smart home. We have proposed a novel approach to developing LogitBoost algorithms, i.e., Logi-XGB, Logi-GBC, Logi-ABC, Logi-CBC, Logi-LGBM, and Logi-HGBC. After applying LogitBoost algorithms to the dataset for the classification, Logi-XGB scored 80.20% accuracy, and Logi-GBC scored 77.80% accuracy. Logi-ABC scored 80.33% accuracy. Logi-CBC scored the highest accuracy of 85.66%. Logi-LGBM and Logi-HGBC scored the same accuracy of 81.37%. Compared with previous LogitBoost algorithms implemented in previous studies, our proposed Logi-CBC has scored the highest accuracy on the given dataset.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.55463/issn.1674-2974.49.4.39
- http://jonuns.com/index.php/journal/article/download/1058/1052
- OA Status
- bronze
- Cited By
- 8
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281286267
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- OpenAlex ID
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https://openalex.org/W4281286267Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.55463/issn.1674-2974.49.4.39Digital Object Identifier
- Title
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An Intelligent Approach for Preserving the Privacy and Security of a Smart Home Based on IoT Using LogitBoost TechniquesWork title
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-30Full publication date if available
- Authors
-
Asif Rahim, Yanru Zhong, Tariq Ahmad, Umar IslamList of authors in order
- Landing page
-
https://doi.org/10.55463/issn.1674-2974.49.4.39Publisher landing page
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https://jonuns.com/index.php/journal/article/download/1058/1052Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://jonuns.com/index.php/journal/article/download/1058/1052Direct OA link when available
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Computer science, Machine learning, Artificial intelligence, Internet of Things, Algorithm, Data mining, Embedded systemTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 4, 2023: 4Per-year citation counts (last 5 years)
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Scaling | 79 |
| abstract_inverted_index.attacks | 164 |
| abstract_inverted_index.dataset | 104, 199 |
| abstract_inverted_index.develop | 111 |
| abstract_inverted_index.devices | 5, 57, 61 |
| abstract_inverted_index.feature | 98, 107 |
| abstract_inverted_index.highest | 219, 247 |
| abstract_inverted_index.machine | 134 |
| abstract_inverted_index.metrics | 118 |
| abstract_inverted_index.network | 64, 139 |
| abstract_inverted_index.perform | 127 |
| abstract_inverted_index.traffic | 33, 41, 47, 65 |
| abstract_inverted_index.various | 49 |
| abstract_inverted_index.vectors | 108 |
| abstract_inverted_index.Compared | 232 |
| abstract_inverted_index.Features | 90 |
| abstract_inverted_index.Logi-ABC | 212 |
| abstract_inverted_index.Logi-CBC | 216, 243 |
| abstract_inverted_index.Logi-GBC | 208 |
| abstract_inverted_index.Logi-XGB | 203 |
| abstract_inverted_index.Multiple | 116 |
| abstract_inverted_index.accuracy | 220, 229, 248 |
| abstract_inverted_index.applying | 194 |
| abstract_inverted_index.approach | 180 |
| abstract_inverted_index.contains | 105 |
| abstract_inverted_index.dataset. | 252 |
| abstract_inverted_index.devices, | 32 |
| abstract_inverted_index.devices. | 29 |
| abstract_inverted_index.distinct | 43 |
| abstract_inverted_index.further. | 115 |
| abstract_inverted_index.learning | 135 |
| abstract_inverted_index.previous | 234, 239 |
| abstract_inverted_index.proposed | 177, 242 |
| abstract_inverted_index.provided | 62 |
| abstract_inverted_index.research | 143 |
| abstract_inverted_index.studies, | 240 |
| abstract_inverted_index.utilized | 109 |
| abstract_inverted_index.IoT-based | 28 |
| abstract_inverted_index.Logi-ABC, | 188 |
| abstract_inverted_index.Logi-CBC, | 189 |
| abstract_inverted_index.Logi-GBC, | 187 |
| abstract_inverted_index.Logi-HGBC | 225 |
| abstract_inverted_index.Logi-LGBM | 223 |
| abstract_inverted_index.Logi-XGB, | 186 |
| abstract_inverted_index.accuracy, | 206 |
| abstract_inverted_index.accuracy. | 211, 215 |
| abstract_inverted_index.acquired. | 89 |
| abstract_inverted_index.analysis, | 22 |
| abstract_inverted_index.anomalies | 140, 166 |
| abstract_inverted_index.construct | 68 |
| abstract_inverted_index.detection | 152 |
| abstract_inverted_index.different | 24 |
| abstract_inverted_index.extracted | 93 |
| abstract_inverted_index.important | 37 |
| abstract_inverted_index.intrusion | 151 |
| abstract_inverted_index.objective | 157 |
| abstract_inverted_index.research. | 130 |
| abstract_inverted_index.respects. | 50 |
| abstract_inverted_index.Logi-HGBC. | 192 |
| abstract_inverted_index.Logi-LGBM, | 190 |
| abstract_inverted_index.LogitBoost | 125, 183, 195, 235 |
| abstract_inverted_index.algorithms | 126, 196, 236 |
| abstract_inverted_index.attributes | 66 |
| abstract_inverted_index.developing | 149, 182 |
| abstract_inverted_index.industries | 26 |
| abstract_inverted_index.multiclass | 70 |
| abstract_inverted_index.techniques | 74 |
| abstract_inverted_index.Development | 0 |
| abstract_inverted_index.algorithms, | 184 |
| abstract_inverted_index.algorithms. | 100 |
| abstract_inverted_index.demonstrate | 122 |
| abstract_inverted_index.departments | 13 |
| abstract_inverted_index.engineering | 99 |
| abstract_inverted_index.environment | 170 |
| abstract_inverted_index.healthcare, | 19 |
| abstract_inverted_index.implemented | 237 |
| abstract_inverted_index.performance | 117 |
| abstract_inverted_index.pre-process | 85 |
| abstract_inverted_index.traditional | 45 |
| abstract_inverted_index.Normalization | 77 |
| abstract_inverted_index.significantly | 8 |
| abstract_inverted_index.Pre-processing | 73 |
| abstract_inverted_index.classification | 71, 113 |
| abstract_inverted_index.ensemble-based | 132 |
| abstract_inverted_index.classification, | 202 |
| abstract_inverted_index.stratification, | 102 |
| abstract_inverted_index.Internet-of-Things | 55 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.79307642 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |