A Thorough Analysis of Deep Learning Models for Blockchain-Based Security Against Adversarial Attacks

This review paper explores the integration of Machine Learning (ML) and Deep Learning (DL) methods with blockchain technology to enhance security against adversarial attacks on DL models. As DL models become more widely adopted, they are increasingly vulnerable to adversarial attacks that compromise their integrity. ML-based blockchain approaches utilize traditional ML algorithms, such as DT and SVM, to detect and mitigate such attacks. These methods are often simpler to implement but may lack the complexity required to handle sophisticated adversarial strategies. On the other hand, DL-based blockchain solutions employ advanced neural networks, including convolutional and recurrent networks, which provide higher accuracy and robustness in detecting intricate attack patterns. DL approaches include drawbacks such as higher computing costs and model interpretability, despite their greater performance. This study examines the ML and DL approaches for protecting DL models, emphasizing how blockchain technology may provide decentralized, transparent, and unchangeable security.