# A Novel Approach for Fraud Detection in Blockchain-Based Healthcare Networks Using Machine Learning
## 2 Related Works  
This section illustrates the existing works related to blockchain security issues. There are many solutions to address security issues in blockchain networks, such as SC auditing tools and machine learning. Table 1 summarizes this paper's related work regarding the main contribution, pros and cons, metrics, and validation tools. According to SC auditing tools, [PERSON] et al. [11] present an innovative approach to detecting malicious SCs in blockchain systems. The DOORchain framework has the potential to improve the security of blockchain systems and protect users from malicious behavior. [PERSON] et al. [12] explored the current state of SC's security, prevalent vulnerabilities, and security analysis tool support. Then, the author studied thirteen crucial vulnerabilities in Ethereum SCs and their countermeasures. The authors investigated nine security-analysis tools to detect vulnerabilities in Ethereum SCs. [PERSON] et al. [13] proposed a novel semantic aware security auditing technique for Ethereum named S-gram for Ethereum. It is a combination of lightweight static semantic labeling and N-gram language modeling. The s-gram technique can predict potential vulnerabilities by identifying irregular token sequences and optimizing existing in-depth analyzers. [PERSON] et al. [8] provide an overview of attacks on SCs and analyses 10 security tools to detect vulnerabilities in SCs, then proposes a set of countermeasures to mitigate these attacks. [PERSON] et al. [14] introduce _A_EGIS, a dynamic analysis tool that protects SCs from being exploited during runtime. Its capability of detecting new vulnerabilities can easily be extended through so-called attack patterns. According to machine learning technology, [PERSON] et al. [15] proposed a KNN- MLSC approach to secure authentication using ML. In their design, the authors used the K-Nearest neighbor (KNN) using an SC to identify and detect the dynamic time attack and authentication in an Internet of Medical Things (IoMT) environment. [PERSON] et al. [16] developed a system based on the Naive Bayes algorithm to perform prediction and protect SCs from cyber-attacks. The system has been implemented in the field of decentralized applications. Therefore, it must blend Blockchain and AI technologies to provide a more secure environment to exchange sensitive data for many applications in healthcare, insurance, etc. [PERSON] et al. [17] designed a framework for privacy-preserving in SCs using a blend of blockchain and AI technologies. The proposed system simplifies human interaction, identifies security risks and service alerts, and detects fraudulent claims. [PERSON] et al. [18] presented a model to analyze the vulnerabilities of SCs in blockchain networks based on machine learning. The proposed model builds the Abstract-Syntax-Trees (ASTs) for SCs. Then, it extracted feature vectors from ASTs to use them for training the ML model. The proposed model can detect many kinds of SCs vulnerabilities written in solidity language in the Ethereum blockchain platform. [PERSON] et al. [19] suggested a new architecture to create a tamperproof and transparent healthcare system using Ethereum SCs. This proposed system can ensure the integrity of sensitive patient data. It also presented a solution to detect and eliminate fraudulent insurance claims using blockchain with ML. [PERSON] et al. [20] The authors present a new model of sequential learning to identify weaknesses of SCs using machine learning, named long-short-term memory (LSTM). The proposed model can detect new attack trends relatively quickly, making SCs safer. [PERSON] et al. [21] surveyed SC's security vulnerabilities in the software code, which a malicious user can easily hack, thereby compromising the entire Blockchain network. Additionally, the authors explored various AI techniques and tools for SC privacy protection. Lastly, open issues and challenges for AI-based SCs are analyzed. [PERSON] et al. [22] suggested a novel algorithm for early detection of DDoS attacks in the healthcare system.