Adversarial Machine Learning for Blockchain Security

This paper explores the intersection of adversarial machine learning (AML) and blockchain security, presenting a comprehensive analysis of the challenges and solutions in leveraging AML techniques to protect blockchain networks. The decentralized and non-regular nature of blockchain technology exposes it to various adversarial threats, including 51% attacks, double spending, and smart contract vulnerabilities. To address these challenges, AML offers a range of techniques, such as adversarial training, generative adversarial networks (GANs), feature engineering, and robust ML models. These techniques enhance the resilience of blockchain systems against adversarial attacks and safeguard sensitive data. However, implementing ML models in blockchain environments presents challenges, including scalability and cross-chain compatibility. The paper discusses these challenges and proposes solutions to overcome them, emphasizing the importance of integrating AML into blockchain security frameworks. Overall, the paper highlights the potential of AML in enhancing the security of blockchain networks and mitigating the evolving threats posed by malicious actors.