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{carofilis-vasco_classifying_nodate,

title = {Classifying Screenshots of Industrial Control System Using Transfer Learning and Fine-Tuning},

author = {Andrés Carofilis-Vasco and Pablo Blanco-Medina and Francisco Jáñez-Martino and Guru Swaroop Bennabhaktula and Eduardo Fidalgo and Alejandro Prieto-Castro and Víctor Fidalgo-Villar},

url = {https://buleria.unileon.es/handle/10612/20274},

abstract = {This paper proposes a deep learning pipeline to classify industrial control panel screenshots into IT, OT, and other categories. Using transfer learning on nine pre-trained CNNs, the model is tested on the CRINF-300 dataset. Inception-ResNet-V2 achieves the best F1-score (98.32%), while MobileNet-V1 offers the best speed-performance balance.},

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

pubstate = {published},

tppubtype = {article}

}