ENHANCING SECURITY AND SCALABILITY IN BLOCKCHAIN NETWORKS THROUGH ARTIFICIAL INTELLIGENCE

Abstract

Blockchain now stands as one of the most revolutionary leading technologies across industries, promising the result that is easier, more transparent, and secure. However, as the network size of blockchains continues to grow, so does the need to address issues of security and scalability of the networks. This research focuses on the possibility to enhance the security and the capacity of the blockchain models using AI methods. In fact, regarding the security threats which are linked to blockchain such as fraud, data leak, and cyber assaults, AI offers new execution styles through machine intelligence learning, deep learning, and anomaly acknowledgment. When it comes to combatting new cyber threats, blockchain environments can add operational value and complexity by staging real-time AI models for threat detection and automated updates to protocols. Secondly, the issue of scalability is significant given that the greater demand for the numbers of the transactions on the Web impedes performance and sometimes leads to a network overload. It also reviews solutions supported by AI technology for instance predictive analysis and optimization programs that can enhance the ability of block chain networks with high through put and the ability to do away with delay hence fast processing of transaction. Further, the interaction between AI and blockchain is explored including how AI can enhance consensus algorithms, control resources, and enable bright contract execution towards scaling the network. The research comprises the case studies of AI implementation, evaluates the present state of developments in AI integration, and considers future advancements in AI integration to fulfill the new needs of blockchain. As shown below, the necessary integration of AI into the blockchain platforms can indeed greatly improve security measures and increase scalability of the current global blockchain platforms which will therefore lead to broader recommendation and sustained usage.