Improving Blockchain Security with AI: Using Machine Learning to Detect and Analyze Threats in Real-Time

The need for robust blockchain network security is growing in tandem with the widespread adoption of blockchain technology. Blockchain has many benefits, such immutability and decentralization, but it also has several weaknesses, such as smart contract flaws, 51% assaults, and double-spending. As blockchain networks grow in size, traditional security methods become more inadequate in detecting and mitigating these attacks in real-time. This study aims to solve this problem by investigating how blockchain security might be improved through the use of Artificial Intelligence (AI), more especially Machine Learning (ML). This project seeks to develop an adaptable system for real-time threat identification, analysis, and mitigation in blockchain environments by utilizing advanced machine learning methods. The use of supervised, unsupervised, and deep learning machine learning models allows for the detection of suspicious or malicious activity patterns in blockchain transactions. In order to identify new security risks, these models examine data from transactions, smart contracts, and network traffic. In addition, the article delves into how reinforcement learning may be utilized to create systems that are constantly learning from the dynamics of blockchain networks, enhancing their capacity to anticipate and counteract attacks. By incorporating ML into blockchain security systems, blockchain networks may better detect and respond to threats in real-time, protecting themselves from assaults that were previously undiscovered. Improving the availability, confidentiality, and integrity of blockchain systems through AI-driven solutions is the focus of this study, which adds to the expanding corpus of information on blockchain environment security.