(UPI) Chapter 13: Detecting Vulnerabilities in Critical Infrastructures Course (How To)

About this Course

In this chapter, a comprehensive pipeline for detecting vulnerabilities in critical infrastructures is presented, focusing on the classification of SCADA images into IT and OT categories using deep learning. The study leverages transfer learning and fine-tuning on a custom dataset (CRINF-300) to evaluate multiple CNN architectures, comparing their performance in terms of accuracy, F1-score, and computational efficiency, and discusses experimental findings and future directions.

Syllabus

13.3 Methodology

In this stage, the chapter outlines the methodology by reviewing the state of the art in CNN-based image classification, detailing the construction of the CRINF-300 dataset, and proposing a pipeline that employs transfer learning and fine-tuning to classify ICS screenshots.

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13.2. State of the Art
Quiz 13.2

5 questions
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13.3.1 Critical Infrastructure Dataset
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13.3.2 Proposed Pipeline
Quiz 13.3

5 questions

13.4. Experimental Results and Discussion

In this stage, the experimental settings, results, and comparisons of various CNN architectures are discussed, highlighting key performance metrics and the trade-offs between speed and accuracy in both transfer learning and fine-tuning approaches.

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