# A Novel Approach for Fraud Detection in Blockchain-Based Healthcare Networks Using Machine Learning
## 5 Security Analysis  
The proposed approach leverages the power of machine learning and blockchain features to identify attacks and their fraudulent behavior and prevent them from occurring. By addressing these concerns, our approach can provide a secure and reliable platform for healthcare providers and patients to interact and share information. We discuss the security analysis which we performed on the proposed BC-based healthcare system as follows:  
1. Identity theft: Identity theft in a blockchain network is the fraudulent acquisition of a user's identity to manipulate their assets or data. The attacker typically steals the user's private key to impersonate them and perform unauthorized transactions. Machine learning algorithms in the proposed system can help detect identity theft attacks on a blockchain network by analyzing user behavior patterns and detecting anomalies that may indicate fraudulent activity;  
Figure 11: Scalability of machine learning algorithms in the proposed system.  
* Distributed Denial of Service (DDoS): DDoS attack in a blockchain network involve overwhelming the network with traffic, rendering it unavailable to users. Blockchain networks can use consensus mechanisms that require participants to perform computational work before contributing to the network, making it more difficult for attackers to mount a DDoS attack. The proposed approach can detect any attempts to flood the network with traffic to disrupt its normal functioning;
* Sybil attacks: Sybil attacks in a blockchain network involve creating multiple identities to control a significant portion of the network's resources. The proposed system can detect any attempts to create multiple fake identities by using identity verification processes to prevent users from creating multiple identities. Additionally, it can detect fake nodes to manipulate the network's consensus mechanism by analyzing network traffic and identifying clusters of nodes that behave abnormally or have suspicious communication patterns;
* Routing attacks: Routing attacks in a blockchain network involve an attacker manipulating the network's routing protocol to redirect traffic to malicious nodes, allowing the attacker to intercept, modify, or block network traffic. This attack cannot be ensured because our proposed approach analyzes network traffic patterns and identifies potential attacks or suspicious behavior;
* Decentralized Autonomous Organization (DAO) attacks: The DAO attack occurs when the attacker exploits a vulnerability in the code of the DAO SC, allowing them to siphon off a large amount of digital currency. The proposed system can detect this type of attack because it analyzes transactions to detect abnormal or fraudulent behavior, such as repeatedly requesting refunds multiple times using different accounts;
* Billing fraud: Billing fraud is when someone submits false or misleading information to receive payment for services that were not provided or were misrepresented. In healthcare blockchain applications, it can occur when a healthcare provider submits false claims for reimbursement. Our system can detect billing fraud by analyzing data and identifying abnormal billing patterns, such as unusually high rates of certain procedures or services, or frequent billing for services that are not typically provided;
* Reentrancy attack: A reentrancy attack is a type of SC vulnerability where an exploiter contract leverages the loophole of the victim contract to continuously withdraw from it until the victim contract goes bankrupt by using machine learning algorithms in the proposed system that analyzes the transactions. It can detect and identify patterns and behaviors of known reentrancy attacks.