RT info:eu-repo/semantics/conferenceProceedings
T1 Classifying Screenshots of Industrial Control System Using Transfer Learning and Fine-Tuning
A1 Carofilis Vasco, Roberto Andrés
A1 Blanco Medina, Pablo
A1 Jáñez Martino, Francisco
A1 Bennabhaktula, Guru Swaroop
A1 Fidalgo Fernández, Eduardo
A1 Prieto Castro, Alejandro
A1 Fidalgo Villar, Victor
A2 SerranoManuel A.
A2 Fernández MedinaEduardo
A2 AlcarazCristina
A2 Castro GarcíaNoemí de
A2 CalvoGuillermo
A2 Ingenieria de Sistemas y Automatica
K1 Ingeniería de sistemas
K1 Ingeniería industrial
K1 Deep Learning
K1 Image Classification
K1 Transfer Learning
K1 Deep learning
K1 Image classification
K1 Transfer learning
K1 Industrial control systems
K1 Fine-tuning
K1 3311.02 Ingeniería de Control
AB [EN] Industrial control systems are heavily dependant on security and monitoring protocols. For this purpose, monitoring tools take screenshots of control panels for later analysis.Classifying these screenshots into specific groups can be a time-consuming process, but it is crucial for the security tasks performed by manual operators. To solve this problem, we propose a pipeline based on deep learning to classify snapshots of industrial control panels into three categories: Internet Technologies (IT), Operation Technologies (OT), and others. We compare the results obtained with transfer learning and finetuning on nine convolutional neuronal networks pre-trained with the ImageNet dataset, testing them on a custom CRitical INFrastructure dataset (CRINF-300). Inception-ResNet-V2 obtains the best learning result with an F1-score of 98.32% on CRINF-300, while MobileNet-V1 obtained the best performance-speed tradeoff
PB Ediciones de la Universidad de Castilla-La Mancha
SN 978-84-9044-463-4
LK https://hdl.handle.net/10612/20274
UL https://hdl.handle.net/10612/20274
NO Vasco-Carofilis, R. A., Blanco-Medina, P., Jáñez-Martino, F., Bennabhaktula, G. S., Fidalgo, E., Prieto Castro, A., & Fidalgo, V. (2021). Classifying Screenshots of Industrial Control System Using Transfer Learning and Fine-Tuning. En Investigación en Ciberseguridad (pp. 151-152). https://doi.org/10.18239/JORNADAS_2021.34.35
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 17-jun-2024