Anomaly detection in IoT device traffic data using transformers
The proliferation of Internet of Things (IoT) devices has significantly increased data traffic, necessitating robust security measures to protect against latent threats. Traditional anomaly detection methods often struggle to keep pace with the dynamic and diverse nature of IoT environments, particu...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/415442 |
| Acceso en línea: | https://hdl.handle.net/2117/415442 |
| Access Level: | acceso abierto |
| Palabra clave: | Internet of things Artificial intelligence Detecció d'anomalias Internet de les Coses Transformadors Transfer learning Ciberseguretat Anomaly detection Internet of Things Transformers Cybersecurity Internet de les coses Intel·ligència artificial Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | The proliferation of Internet of Things (IoT) devices has significantly increased data traffic, necessitating robust security measures to protect against latent threats. Traditional anomaly detection methods often struggle to keep pace with the dynamic and diverse nature of IoT environments, particularly in use cases with limited accessible training data. This necessitates adaptive and efficient solutions. In this work, we propose the use of fine-tuned Transformer models for anomaly detection in an IoT traffic use-case. These techniques allow adjusting a pre-trained model to a new domain where scarce training data is accessible. Specifically, we propose a framework in which we explore the usage of fine-tuning Transformer architectures using the CIC IoMT 2024 dataset and evaluate it with the Aposemat IoT-23 dataset. The significance of the results lies in demonstrating that training on one dataset and applying the knowledge to another is feasible, though it involves aligning certain specifications between datasets and employing techniques to adapt to new knowledge. This research bridges the gap in developing new Transformer-based architectures capable of providing supervised fraud detection capabilities, even with highly limited datasets for training. This process, including both successful and unsuccessful methods, is described in this thesis. |
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