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Detection of Security Attacks in Industrial IoT Networks: A Blockchain and Machine Learning Approach
Content Provider | MDPI |
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Author | Vargas, Henry Lozano-Garzon, Carlos Montoya, Germán A. Donoso, Yezid |
Copyright Year | 2021 |
Description | Internet of Things (IoT) networks have been integrated into industrial infrastructure schemes, positioning themselves as devices that communicate highly classified information for the most critical companies of world nations. Currently, and in order to look for alternatives to mitigate this risk, solutions based on Blockchain algorithms and Machine Learning techniques have been implemented separately with the aim of mitigating potential threats in IIoT networks. In this paper, we sought to integrate the previous solutions to create an integral protection mechanism for IoT device networks, which would allow the identification of threats, activate secure information transfer mechanisms, and it would be adapted to the computational capabilities of industrial IoT. The proposed solution achieved the proposed objectives and is presented as a viable mechanism for detecting and containing intruders in an IoT network. In some cases, it overcomes traditional detection mechanisms such as an IDS. |
Starting Page | 2662 |
e-ISSN | 20799292 |
DOI | 10.3390/electronics10212662 |
Journal | Electronics |
Issue Number | 21 |
Volume Number | 10 |
Language | English |
Publisher | MDPI |
Publisher Date | 2021-10-30 |
Access Restriction | Open |
Subject Keyword | Electronics Industrial Engineering Information and Library Science Blockchain Industrial Internet of Things (iiot) Intrusion Machine Learning |
Content Type | Text |
Resource Type | Article |