A Novel Approach for Fraud Detection in Blockchain-Based Healthcare Networks Using Machine Learning

Abstract

Recently, the advent of blockchain (BC) has sparked a digital revolution in different fields, such as finance, healthcare, and supply chain. It is used by smart healthcare systems to provide transparency and control for personal medical records. However, BC and healthcare integration still face many challenges, such as storing patient data and privacy and security issues. In the context of security, new attacks target different parts of the BC network, such as nodes, consensus algorithms, Smart Contracts (SC), and wallets. Fraudulent data insertion can have serious consequences on the integrity and reliability of the BC, as it can compromise the trustworthiness of the information stored on it and lead to incorrect or misleading transactions. Detecting and preventing fraudulent data insertion is crucial for maintaining the credibility of the BC as a secure and transparent system for recording and verifying transactions. SCs control the transfer of assets, which is why they may be subject to several adverbial attacks. Therefore, many efforts have been proposed to detect vulnerabilities and attacks in the SCs, such as utilizing programming tools. However, their proposals are inadequate against the newly emerging vulnerabilities and attacks. Artificial Intelligence technology is robust in analyzing and detecting new attacks in every part of the BC network. Therefore, this article proposes a system architecture for detecting fraudulent transactions and attacks in the BC network based on Machine Learning (ML). It is composed of two stages: (1) Using ML to check medical data from sensors and block abnormal data from entering the blockchain network. (2) Using the same ML to check transactions in the blockchain, storing normal transactions, and marking abnormal ones as novel attacks in the attacks database. To build our system, we utilized two datasets and six machine learning algorithms (Logistic Regression, Decision Tree, KNN, Naive Bayes, SVM, and Random Forest). The results demonstrate that the Random Forest algorithm outperformed others by achieving the highest accuracy, execution time, and scalability. Thereby, it was considered the best solution among the rest of the algorithms for tackling the research problem. Moreover, the security analysis of the proposed system proves its robustness against several attacks which threaten the functioning of the blockchain-based healthcare application.