Machine Learning Approaches for Bitcoin Price Forecasting with Enhanced Cybersecurity and Privacy Solutions in Blockchain

Abstract

This study presents a comprehensive framework that integrates deep learning and blockchain security to address key challenges in cryptocurrency forecasting and privacy preservation. A state-of-the-art ensemble machine learning model, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, is proposed for Bitcoin price prediction. The model achieves 92.1% accuracy on out-of-sample data following rigorous validation, demonstrating strong forecasting performance. To address fundamental security and privacy concerns in blockchain systems, a dynamic privacy framework is proposed, which integrates Zero-Knowledge Proofs (ZKPs) and adaptable consensus methods to improve transaction confidentiality, scalability, and adherence to regulations.