Secure Sentinel Leveraging Machine Learning for Fraud Detection in Blockchain Transactions

Blockchain technology's decentralized and transparent transaction platforms have changed a number of sectors. However, harmful activities like fraud and money laundering are also drawn to blockchain due to its distributed and unchangeable nature. The distinctive features of blockchain transactions frequently prove to be too much for conventional fraud detection techniques to handle. In order to identify fraud in blockchain transactions, this work suggests "Secure Sentinel," a machine learning-based method based on the Isolation Forest concept. By separating anomalies in a binary tree structure, the Isolation Forest algorithm is highly effective in detecting abnormalities inside datasets. By utilizing this approach, our system is able to identify transactions on the blockchain that are suspicious or stray from the norm. The Isolation Forest model will be trained using attributes such transaction amount, frequency, source/destination addresses, and transaction time. The project's goal is to improve blockchain networks' security and integrity by offering a real-time fraud detection system. Users, miners, and regulators are among the blockchain stakeholders who can take proactive steps to reduce risks and preserve system confidence by automatically identifying potentially fraudulent transactions. We will use past blockchain data to test our model's performance and determine how well it detects different kinds of fraudulent activity in terms of accuracy, precision, recall, and efficiency. By using machine learning to protect blockchain ecosystems from new dangers, Secure Sentinel is a step in the right direction toward advancing the acceptance and sustainability of blockchain technology.