FORTIFYING BITCOIN TRANSACTIONS: ADVANCED MACHINE LEARNING TECHNIQUES FOR FRAUD DETECTION
Keywords:
Bitcoin, Cryptocurrency, Fraud Detection, Blockchain, Transaction Security, Anomaly Detection, Machine Learning, Graph Analysis, Privacy Enhancements, Data Transparency, Decentralized Finance (DeFi), Digital Assets, Cryptocurrency Forensics, Financial Fraud, Fraudulent SchemesAbstract
With the growing adoption of cryptocurrencies, Bitcoin has emerged as a prominent player in the global financial landscape. However, its decentralized and pseudonymous nature has made it an attractive target for fraudulent activities. This paper presents a comprehensive exploration of fraud detection techniques specifically tailored to Bitcoin transactions.
In this research, we delve into the intricacies of the Bitcoin network, analyzing transaction data, and identifying patterns that indicate potential fraudulent behaviour. We propose a multifaceted approach that combines machine learning algorithms, graph analysis, and heuristic rule-based systems to detect various types of fraud, including Ponzi schemes, money laundering, and unauthorized transfers.
Our study leverages the transparency of the blockchain to extract relevant features and build models capable of identifying anomalous transactions. Furthermore, we address the challenges posed by the dynamic nature of Bitcoin transactions, such as mixing services and privacy enhancements, which attempt to obfuscate transaction trails. We discuss strategies for adapting our fraud detection techniques to these evolving tactics, ensuring the continued effectiveness of our approach.
To validate our methodology, we present empirical results based on a comprehensive dataset of real-world Bitcoin transactions. We demonstrate the efficacy of our approach in detecting fraudulent activities and showcase its potential to enhance the security and trustworthiness of Bitcoin as a digital asset.
In conclusion, this paper contributes to the growing body of research aimed at safeguarding the integrity of cryptocurrency networks. By proposing advanced fraud detection techniques tailored to Bitcoin transactions, we take a significant step toward mitigating the risks associated with cryptocurrency use, fostering trust among users, and facilitating its broader adoption in the global financial ecosystem.
