Anomaly Detection in IoT: Methods, Techniques and Tools
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http://hdl.handle.net/2183/23964Colecciones
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Anomaly Detection in IoT: Methods, Techniques and ToolsFecha
2019-07-22Cita bibliográfica
Morales, L.V.V.; López-Vizcaíno, M.; Iglesias, D.F.; Díaz, V.M.C. Anomaly Detection in IoT: Methods, Techniques and Tools. Proceedings 2019, 21, 4.
Resumen
[Abstract] Nowadays, the Internet of things (IoT) network, as system of interrelated computing devices with the ability to transfer data over a network, is present in many scenarios of everyday life. Understanding how traffic behaves can be done more easily if the real environment is replicated to a virtualized environment. In this paper, we propose a methodology to develop a systematic approach to dataset analysis for detecting traffic anomalies in an IoT network. The reader will become familiar with the specific techniques and tools that are used. The methodology will have five stages: definition of the scenario, injection of anomalous packages, dataset analysis, implementation of classification algorithms for anomaly detection and conclusions.
Palabras clave
Intrusion Detection Systems
Analysis
Metric
Algorithm design
Computer network management
Analysis
Metric
Algorithm design
Computer network management
Versión del editor
Derechos
Atribución 3.0 España
ISSN
2504-3900