Ponencia
A Summary of Adversarial Attacks and Defenses on ML- and Hardware-based IoT Device Fingerprinting and Identification [Póster]
Autor/es | Sánchez Sánchez, Pedro Miguel
Huertas Celdrán, Alberto Bovet, Gérôme Martínez Pérez, Gregorio |
Coordinador/Director | Varela Vaca, Ángel Jesús
![]() ![]() ![]() ![]() ![]() ![]() ![]() Ceballos Guerrero, Rafael ![]() ![]() ![]() ![]() ![]() ![]() ![]() Reina Quintero, Antonia María ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
Fecha de publicación | 2024 |
Fecha de depósito | 2024-07-18 |
Publicado en |
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ISBN/ISSN | 978-84-09-62140-8 |
Resumen | In response to the rapid expansion of Internet of-Things (IoT) devices and associated cybersecurity threats, this work proposes a novel LSTM-CNN architecture for robust individual device identification, leveraging behavior ... In response to the rapid expansion of Internet of-Things (IoT) devices and associated cybersecurity threats, this work proposes a novel LSTM-CNN architecture for robust individual device identification, leveraging behavior monitoring and ML/DL advancements. Evaluated against a dataset from 45 Raspberry Pi devices, this model outperforms traditional ML/DL methods, achieving a +0.96 average F1-Score and demonstrating strong resilience to adversarial attacks, including context-based and ML/DL-specific evasion attempts. Through the application of adversarial training and model distillation defenses, the model vulnerability to the most effective attack was reduced from a 0.88 success rate to 0.17, maintaining high-performance integrity. |
Cita | Sánchez Sánchez, P.M., Huertas Celdrán, A., Bovet, G. y Martínez Pérez, G. (2024). A Summary of Adversarial Attacks and Defenses on ML- and Hardware-based IoT Device Fingerprinting and Identification [Póster]. En Jornadas Nacionales de Investigación en Ciberseguridad (JNIC) (9ª.2024. Sevilla) (450-451), Sevilla: Universidad de Sevilla. Escuela Técnica Superior de Ingeniería Informática. |
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