AN CATBOOST AND MLP OPTIMIZATION WITH ML AND DL APPROACHES ON PRIVACY PRESERVING AND CRYPTANALYSIS

Abstract

With the increasing deployment of machine learning (ML) and deep learning (DL) models in security-sensitive domains, ensuring privacy and robust cryptanalysis has become crucial. This paper presents an integrated approach using CatBoost, a gradient boosting algorithm, and Multi-Layer Perceptron (MLP), a deep learning model, optimized for applications in privacy-preserving analysis and cryptanalysis. The proposed framework leverages hybrid ML-DL strategies to enhance model accuracy, robustness, and interpretability in data-sensitive environments. Empirical evaluations demonstrate superior performance in recognizing cryptographic patterns and ensuring data security through adversarial resilience and model interpretability. We propose optimization techniques for both models using hyperparameter tuning and regularization under privacy constraints.