Security Threat Detection Performance Analysis of a Distributed Architecture WSN
[EN] IoT technologies are becoming more and more common in our daily activities because the networks they create are capable of collecting information, monitoring and controlling remotely. However, these devices are not exempt from security attacks, as they become vulnerable entry points to data net...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/219894 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/219894 |
| Access Level: | acceso abierto |
| Palabra clave: | IoT Distributed WSN IDS NodeMCU Machine Learning ANNK-means 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación |
| Sumario: | [EN] IoT technologies are becoming more and more common in our daily activities because the networks they create are capable of collecting information, monitoring and controlling remotely. However, these devices are not exempt from security attacks, as they become vulnerable entry points to data networks. The use of traditional methods to secure networks (e.g., Next Generation Firewalls (NGFW), encryption, etc.) is not recommended because the devices used in this type of network are limited in terms of computing power and storage availability (e.g., nodeMCU). In this paper, we propose to design two intrusion detection systems in embedded systems using machine learning (ML) algorithms, Artificial Neural Networks and K-means. In a distributed architecture Wireless Sensor Network scenario (WSN), we evaluate their performance in terms of connection and response times, detection accuracy and intruder detection time. Simulation results show that both models are able to find irregularities in network traffic within milliseconds. |
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