Classification of Industrial Control Systems screenshots using Transfer Learning
Pablo Blanco Medina, Eduardo Fidalgo Fernandez, Enrique Alegre,, Francisco J\'a\~nez Martino, Roberto A. Vasco-Carofilis, V\'ictor Fidalgo, Villar

TL;DR
This paper evaluates transfer learning with five CNN architectures to classify ICS screenshots, finding MobilenetV1 most accurate and VGG16 fastest on GPU, aiding security monitoring.
Contribution
It compares five CNN architectures for classifying ICS screenshots, identifying the best models based on accuracy and processing speed.
Findings
MobilenetV1 achieved 97.95% F1-Score.
VGG16 processed images in 0.04 seconds on GPU.
MobilenetV1 balanced accuracy and speed effectively.
Abstract
Industrial Control Systems depend heavily on security and monitoring protocols. Several tools are available for this purpose, which scout vulnerabilities and take screenshots from various control panels for later analysis. However, they do not adequately classify images into specific control groups, which can difficult operations performed by manual operators. In order to solve this problem, we use transfer learning with five CNN architectures, pre-trained on Imagenet, to determine which one best classifies screenshots obtained from Industrial Controls Systems. Using 337 manually labeled images, we train these architectures and study their performance both in accuracy and CPU and GPU time. We find out that MobilenetV1 is the best architecture based on its 97,95% of F1-Score, and its speed on CPU with 0.47 seconds per image. In systems where time is critical and GPU is available, VGG16…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAnomaly Detection Techniques and Applications · Industrial Vision Systems and Defect Detection · Fault Detection and Control Systems
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings · Pointwise Convolution · Depthwise Convolution · Softmax · *Communicated@Fast*How Do I Communicate to Expedia? · Batch Normalization · Average Pooling · Depthwise Separable Convolution · 1x1 Convolution · Dense Connections
