Machine learning model on blockchain for secured mobile communication
| Autor: | Amit Barve, Manish Shrivastava, Abhilash Kumar Saxena, S. Sivaperumal |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Measurement: Sensors, Vol 33, Iss , Pp 101160- (2024) |
| Druh dokumentu: | article |
| ISSN: | 2665-9174 19560265 |
| DOI: | 10.1016/j.measen.2024.101160 |
| Popis: | The Internet of Things (IoT) is an open network model that aims to build and link the interactions between the devices and links. Conventional blockchain models aimed at increasing scalability but often it is limited by its capacity and performance. The machine learning algorithms aim to determine the parameters of the blockchain that finds the optimal value required to obtain an increased scalability without any limitations in its performance. In this paper, a machine learning model called Block Chain-Support Vector Machine (BC-SVM) is integrated with the blockchain to improve the process of communication in a secure way. Here Modified elliptic-curve cryptography (ECC) is a public key encryption technique based on elliptic curve theory and may be utilised for creating cryptographic keys more quickly and efficiently, increasing security. The machine learning model optimizes the necessary security parameters required to transfer the data in a secure way. The experimental validation shows an increased scalable task allocation than its predecessors. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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