Securing the Chain: Machine Learning Powered Defense Against Blockchain Cyberattacks

As blockchain technology continues to gain significance in various sectors, protecting its infrastructure from potential future cyberattacks is an inherent problem. In this paper, existing security algorithms and mechanisms designed to secure blockchain networks against cyberattacks, such as DDoS, Sybil, and spoofing, are critically analyzed. By analyzing the merits and demerits of existing approaches like machine learning-based intrusion detection, decentralized cooperative frameworks, and behavioral analysis methods, the research finds the most effective approaches to counteract the vulnerabilities that come with blockchain's peer-to-peer nature. A primary emphasis is on balancing security innovation with implementable solutions, making proposed solutions work with blockchain's decentralized ethos while solving scalability, privacy, and interoperability challenges. The paper discusses artificial intelligence's role in discovering previously unknown cyberattacks and enhancing threat detection using deep learning models. This paper offers a comprehensive analysis of current security weaknesses, newly developing attack vectors, and developing defense tools in the hope of enabling the creation of robust and dynamic security models. Enhancing blockchain security is imperative for building trust, resilience, and broad acceptance across major industries.