Classification of Industrial Control Systems screenshots using Transfer Learning

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 is preferable because it takes 0.04 seconds to process images, but dropping performance to 87,67%.