A Secure Approach for Cross-chain Transactions Using Machine Learning Model

Abstract

The ability to conduct transactions or transfer assets between different blockchain networks is referred to as cross-chain transactions. It enables users to transfer assets from one blockchain network to another. In the Cryptocurrency ecosystem, the risk of fraudulent activities has become a significant concern. Due to these fraudulent activities, the cross-chain transactions have encountered challenges in terms of security and integrity. The need for robust fraud detection mechanisms becomes crucial to secure the integrity of transactions and protect investors from fraudulent activities. Detecting those fraudulent activities and preventing them is a challenging task due to the decentralized and pseudonymous nature of these digital assets. Machine Learning algorithms emerged as a power tool for fraud detection across various domains. This study proposes a novel secure approach for fraud detection in cryptocurrency transactions by leveraging machine learning algorithms. Our proposed methodology is to identify malicious activities and discern fraudulent transactions from legitimate ones. The machine learning models are trained on labelled datasets comprising both fraudulent and legitimate transactions by allowing them to learn patterns and detect anomalies indicative of fraudulent behavior. This proposed methodology contributes to the growing body of knowledge in cryptocurrency fraud detection and acts as a foundation for developing robust security measures and risk mitigation strategies to make the cross-chain transactions much stronger.