Industrial control systems depend heavily on security and monitoring protocols. Several tools are available for this purpose, which scout vulnerabilities and take screenshots of various control panels for later analysis. However, they do not adequately classify images into specific control groups, which is crucial for security-based 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, operation technologies, and others. More specifically, we compare the use of transfer learning and fine-tuning in convolutional neural networks (CNNs) pre-trained on ImageNet to select the best CNN architecture for classifying the screenshots of industrial control systems. We propose the critical infrastructure dataset (CRINF-300), which is the first publicly available information technology (IT)/operational technology (OT) snapshot dataset, with 337 manually labeled images. We used the CRINF-300 to train and evaluate eighteen different pipelines, registering their performance under CPU and GPU environments. We found out that the Inception-ResNet-V2 and VGG16 architectures obtained the best results on transfer learning and fine-tuning, with F1-scores of 0.9832 and 0.9373, respectively. In systems where time is critical and the GPU is available, we recommend using the MobileNet-V1 architecture, with an average time of 0.03 s to process an image and with an F1-score of 0.9758.

Inception module is one of the most used variants in convolutional neural networks. It has a large portfolio of success cases in computer vision. In the past years, diverse inception flavours, differing in the number of branches, the size and the number of the kernels, have appeared in the scientific literature. They are proposed based on the expertise of the practitioners without any optimization process. In this work, an implementation of population-based incremental learning is proposed for automatic optimization of the hyperparameters of the inception module. This hyperparameters optimization undertakes classification of the MNIST database of handwritten digit images. This problem is widely used as a benchmark in classification, and therefore, the learned best configurations for the Inception module will be of wide use in the deep learning community. In order to reduce the carbon footprint of the optimization process, policies for reducing the redundant evaluations have been undertaken. As a consequence of this work, an evaluation of configurations of the inception module and a mechanism for optimizing hyperparameters in deep learning architectures are stated.

The optimization of hyperparameters in Deep Neural Net-works is a critical task for the final performance, but it involves a high amount of subjective decisions based on previous researchers' expertise. This paper presents the implementation of Population-based Incremen-tal Learning for the automatic optimization of hyperparameters in Deep Learning architectures. Namely, the proposed architecture is a combina-tion of preprocessing the time series input with Seasonal Decomposition of Time Series by Loess, a classical method for decomposing time series, and forecasting with Convolutional Neural Networks. In the past, this combination has produced promising results, but penalized by an incre-mental number of parameters. The proposed architecture is applied to the prediction of the 222 Rn level at the Canfranc Underground Labora-tory (Spain). By predicting the lowlevel periods of 222 Rn,the potential contamination during the maintenance operations in the experiments hosted in the laboratory could be minimized. In this paper, it is shown that Population-based Incremental Learning can be used for the choice of optimized hyperparameters in Deep Learning architectures with a reasonable computational cost.

Colección JORNADAS Y CONGRESOS n.º 34 Esta editorial es miembro de la UNE, lo que garantiza la difusión y comercialización de sus publicaciones a nivel nacional e internacional.

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