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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.