Classifying Screenshots of Industrial Control System Using Transfer Learning and Fine-Tuning

Abstract

[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