Publications

@article{blanco-medina_detecting_2021,

title = {Detecting vulnerabilities in critical infrastructures by classifying exposed industrial control systems using deep learning},

author = {Pablo Blanco-Medina and Eduardo Fidalgo and Enrique Alegre and Andrés Carofilis-Vasco and Francisco Jáñez-Martino and Víctor Fidalgo-Villar},

url = {https://www.mdpi.com/2076-3417/11/1/367},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Applied Sciences},

volume = {11},

number = {1},

pages = {367},

abstract = {This paper presents a deep learning pipeline to classify industrial control panel screenshots into three categories: internet technologies, operation technologies, and others. Using the CRINF-300 dataset, the authors compared CNN architectures and found that Inception-ResNet-V2 and VGG16 performed best, while MobileNet-V1 was recommended for time-sensitive systems with GPU availability.},

note = {Publisher: MDPI},

keywords = {deep learning, Fine-tuning, Image classification, Industrial Control System, Transfer Learning},

pubstate = {published},

tppubtype = {article}

}

@article{blanco-medina_classification_2020,

title = {Classification of Industrial Control Systems screenshots using Transfer Learning},

author = {Pablo Blanco-Medina and Eduardo Fidalgo and Enrique Alegre and Francisco Jáñez-Martino and Andrés Carofilis-Vasco and Víctor Fidalgo-Villar},

year  = {2020},

date = {2020-01-01},

journal = {arXiv e-prints},

pages = {arXiv–2005},

abstract = {This study evaluates CNN-based transfer learning for classifying Industrial Control System screenshots. Five pre-trained architectures are tested, with MobileNetV1 achieving the best balance of accuracy (97.95% F1-score) and CPU speed (0.47s). For GPU-dependent, time-critical tasks, VGG16 is faster (0.04s) but less accurate (87.67%).},

keywords = {Image classification, Industrial Control System, Transfer Learning},

pubstate = {published},

tppubtype = {article}

}