AI Enabled Security for Ethereum Blockchain Transactions

Abstract

This article seeks to discuss the opportunity for security enhancement in the Ethereum blockchain by introducing AI into the Ethereum blockchain
ecosystem. Integrating AI with blockchain includes revolutionary approaches to protecting transactions by using techniques like anomaly detection, fraud, and predictive analysis. Hopwood et al. provide a background to blockchain technology with much focus on security in decentralized networks, especially Ethereum. It explores the basics of blockchain security based on cryptographic techniques, consensus algorithms, and the weaknesses of smart contracts. The discussion then turns to opportunities for AI technologies in blockchain security and the example of how the technologies can identify and prevent various activities. The most elaborate part of the paper is the Sequencer Level Security (SLS) protocol, a relatively new one that offers an improved
model of transaction security that isolates the undesirable ones. The rollups and Layer 2 solutions involve the presented case of the Zircuit prototype and the implementation of SLS. The paper also discusses how AI may enhance personal data protection in blockchain environments via methods such as decentralized identity and zero-knowledge proofs. Legal and ethical issues are discussed with reference to data protection laws, including GDPR and CPRA, and their effects on the incorporation of AI and blockchain systems. Finally, it envisions the future trends, issues, and opportunities of AI and blockchain security based on a suggested research agenda. Based on this all-around assessment, AI plays a pivotal part in enhancing the security and privacy of Ethereum blocks.