A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism

Abstract

In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms—XGboost and random forest (RF)—used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities.

A novel approach to detect fraud in Ethereum transactions using stacking

“…The authors Poursafaei et al (2020) have used several classification algorithms such as Logistic Regression, Random Forest, Support Vector ing algorithm performed better than the K-means algorithm when tested on a set that contained transactions of known fraudsters with it successfully detecting five fraudsters out of the total 30. The authors Ashfaq et al (2022) have worked on a similar problem statement as that of Monamo et al (2016). They have used SMOTE to overcome the problem of an imbalanced dataset which is followed by training models using XGBoost and Random Forest algorithms to identify malicious transactions.…”

Development of hyper‐parameter‐tuned‐recurrent neural network for detection and mitigation of fraudulent resource consumption attack in cloud

“…In 2022, Rabiya 26 has developed a secured fraud detection method using ML and blockchain. Here, two ML algorithms were introduced, such as XGboost and Random Forest.…”

“…Thus, it tends to improve the performance that effectively detects the FRC attack. The objective function of HT-RNN is modeled in Equation (26). Hidden layer and its neuron is optimized using ADHOA…”

A Progressive Web3: From Social Coproduction to Digital Polycentric Governance

“…Most research uses supervised machine learning techniques to classify addresses, but with the exception of Akcora et al [1] and Paquet-Clouston et al [42], researchers do not have access to quality labelled datasets. Instead, researchers use synthetic and fake data: Ashfaq et al [2] use a synthetic dataset; Rabieinejad et al [44] generate fake labels; Dahiya et al [11] use an unverified Kaggle dataset; Pham and Lee [43] use unverified labels for "30 thieves" of unknown provenance; Sankar Roy et al [47] use the same Pham and Lee dataset. Or researchers use very simple heuristics (such as node degree patterns, see Weber et al [55] and Lorenz et al [34] who use the Weber et al dataset) or slightly more complex heuristics (like motifs, see Wu et al [58]) and assume that such patterns are evidence of complex criminal behaviour, like money laundering.…”