Support Vector Machine Classification using Proximity Authentication and Surveillance System in IoT Industrial Network
| Autor: | Salem Jeyaseelan W. R., Sudhakaran P., Rajakani V., Parameswari A. |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Tehnički Vjesnik, Vol 31, Iss 1, Pp 233-239 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1330-3651 1848-6339 |
| DOI: | 10.17559/TV-20230602000691 |
| Popis: | This research focuses on developing a proximity-based authentication and surveillance system using Internet of Things (IoT) devices in industrial networks. The system aims to improve security measures by ensuring only authorized personnel have access to critical areas of the network. Researching authentication mechanisms and protocols, examining network authentication system features and application environments, and developing an online-based, real-time monitoring authentication system are the goals of this article. The system will utilize sensors and cameras installed in strategic locations to detect and track personnel movement within the network. When a person approaches a secured area, their identity will be verified using proximity-based authentication using RFID technology. The system will also monitor and record any suspicious activity, providing real-time alerts to security personnel. To detect malicious behavior on short-range, low-rate, and low-power networks, such as those found in the Internet of Things (IoT), we advise utilizing SVM models. The proposed system is expected to increase security and reduce the risk of unauthorized access to industrial networks, ultimately enhancing overall network reliability and safety. The results are compared with various parameters. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|