A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices
The inter-class classification and source identification of IoT devices has been studied by several researchers recently due to the vast amount of available IoT devices and the huge amount of data these IoT devices generate almost every minute. As such there is every need to identify the source wher...
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| Format: | Article |
| Language: | English |
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Sakarya University
2023-12-01
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| Series: | Sakarya University Journal of Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/3383578 |
| _version_ | 1797200656614817792 |
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| author | Aamo Iorliam |
| author_facet | Aamo Iorliam |
| author_sort | Aamo Iorliam |
| collection | DOAJ |
| description | The inter-class classification and source identification of IoT devices has been studied by several researchers recently due to the vast amount of available IoT devices and the huge amount of data these IoT devices generate almost every minute. As such there is every need to identify the source where the IoT data is generated and also separate an IoT device from the other using on the data they generate. This paper proposes a novel additive IoT features with the CNN system for the purpose of IoT source identification and classification. Experimental results shows that indeed the proposed method is very effective achieving an overall classification and source identification accuracy of 99.67 %. This result has a practical application to forensics purposes due to the fact that accurately identifying and classifying the source of an IoT device via the generated data can link organisations/persons to the activities they perform over the network. As such ensuring accountability and responsibility by IoT device users. |
| first_indexed | 2024-03-08T13:40:14Z |
| format | Article |
| id | doaj.art-616cfc9c4c184b8eb8441f870c9fc691 |
| institution | Directory Open Access Journal |
| issn | 2636-8129 |
| language | English |
| last_indexed | 2024-04-24T07:35:07Z |
| publishDate | 2023-12-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya University Journal of Computer and Information Sciences |
| spelling | doaj.art-616cfc9c4c184b8eb8441f870c9fc6912024-04-20T09:12:04ZengSakarya UniversitySakarya University Journal of Computer and Information Sciences2636-81292023-12-016321822510.35377/saucis...135479128A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT DevicesAamo Iorliam0Benue State University, MakurdiThe inter-class classification and source identification of IoT devices has been studied by several researchers recently due to the vast amount of available IoT devices and the huge amount of data these IoT devices generate almost every minute. As such there is every need to identify the source where the IoT data is generated and also separate an IoT device from the other using on the data they generate. This paper proposes a novel additive IoT features with the CNN system for the purpose of IoT source identification and classification. Experimental results shows that indeed the proposed method is very effective achieving an overall classification and source identification accuracy of 99.67 %. This result has a practical application to forensics purposes due to the fact that accurately identifying and classifying the source of an IoT device via the generated data can link organisations/persons to the activities they perform over the network. As such ensuring accountability and responsibility by IoT device users.https://dergipark.org.tr/tr/download/article-file/3383578internet of things (iot)additive iot featuresinter-class classificationsource identification |
| spellingShingle | Aamo Iorliam A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices Sakarya University Journal of Computer and Information Sciences internet of things (iot) additive iot features inter-class classification source identification |
| title | A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices |
| title_full | A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices |
| title_fullStr | A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices |
| title_full_unstemmed | A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices |
| title_short | A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices |
| title_sort | novel additive internet of things iot features and convolutional neural network for classification and source identification of iot devices |
| topic | internet of things (iot) additive iot features inter-class classification source identification |
| url | https://dergipark.org.tr/tr/download/article-file/3383578 |
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