Classifying Transactional Addresses using Supervised Learning Approaches over Ethereum Blockchain

Saxena, Rohit; Arora, Deepak; Nagar, Vishal

doi:10.1016/j.procs.2023.01.178

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…Supervised machine learning models, including linear, non-linear, and ensemble models classified harmful and non-harmful activities. This study showed that linear and non-linear machine learning outperformed ensemble learning in classifying Ethereum blockchain addresses [5]. All methodologies are contingent upon the availability and utilization of data.…”

mentioning

confidence: 90%

K-Means Clustering and Multilayer Perceptron for Categorizing Student Business Groups

Walid,

Sahbaniya,

Hozairi

et al. 2023

Knowl. Eng. Data Sci.

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The research conducted in this study was driven by the East Java provincial government's requirement to assess the transaction levels of the Student Business Group (KUS) in the SMA Double Track program. These transaction levels are a basis for allocating supplementary financial aid to each business group. The system's primary objective is to assist the provincial government of East Java in making well-informed choices pertaining to the distribution of supplementary capital to the KUS. The classification technique employed in this study is the multilayer perceptron. However, the K-Means Clustering method is utilised to generate target data due to the limited availability during the classification process, which involves dividing the transaction level attributes into three distinct groups: (0) low transactions, (1) medium transactions, and (2) high transactions. The clustering process encompasses three distinct features: (1) income, (2) spending, and (3) profit. These three traits will be utilized as input data throughout the categorization procedure. The classification procedure employing the Multilayer Perceptron technique involved processing a dataset including 1383 data points. The training data constituted 80% of the dataset, while the remaining 20% was allocated for testing. In order to evaluate the efficacy of the constructed model, the training error was assessed using K-Fold cross-validation, yielding an average accuracy score of 0.92. In the present study, the categorization technique yielded an accuracy of 0.96. This model aims to classify scenarios when the dataset lacks prior target data.

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mentioning

confidence: 90%

K-Means Clustering and Multilayer Perceptron for Categorizing Student Business Groups

Walid,

Sahbaniya,

Hozairi

et al. 2023

Knowl. Eng. Data Sci.

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“…The authors used ML algorithms, including DT and SVM, to identify potentially fraudulent transactions based on the features extracted from the various transaction perspectives. The authors proposed a supervised learning approach in [27] that used various ML algorithms, including LR, DT, and SVM, to classify transactional addresses based on their transactional behavior. The authors used various features extracted from the transactional behavior, including the number of transactions, the transaction frequency, and the transaction value, to train the ML models.…”

Section: Related Workmentioning

confidence: 99%

“…Proposed SINN-RD [24] Accuracy, Recall and Precision Existing techniques do not tackle Blackbox issue Used LR, KNN, RF, and NN [25] ROC, F1score, Specificity and Recall Existing techniques are not efficient for handling big data Used SVM [26] Precision, Recall, F1-score and AUC Binary classification issue in cryptocurrency Used ML techniques (LR, KNN, NB, RF and XGBoost) [27] Accuracy, F1-score, Precision and Recall Limited accuracy and performance of individual classifiers Used SNN and optimizeable DT [29] Precision, Recall, F1-score, and AUC. Existing research do not concentrate on fraud detection Used RF and XGBoost [30] Accuracy, F1-score and AUC-ROC curve Issue in Virtual Private Network (VPN) tunneling Used SVM, KNN, NB and RF [32] MSE, TP and FP illicit activities in [31].…”

Section: Poor Performance Of Existing Techniques On Big Datamentioning

confidence: 99%

A New Framework for Fraud Detection in Bitcoin Transactions Through Ensemble Stacking Model in Smart Cities

Nayyer,

Javaid,

Akbar

et al. 2023

IEEE Access

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Bitcoin has a reputation of being used for unlawful activities, such as money laundering, dark web transactions, and payments for ransomware in the context of smart cities. Blockchain technology prevents illegal transactions, but cannot detect these transactions. Anomaly detection is a fundamental technique for recognizing potential fraud. The heuristic and signature-based approaches were the foundation of earlier detection techniques, but tragically, these methods were insufficient to explore the entire complexity of anomaly detection. Machine Learning (ML) is a promising approach to anomaly detection, as it can be trained on large datasets of known malware samples to identify patterns and features of the transactions. Researchers are focusing on determining an efficient fraud and security threat detection model that overcomes the drawbacks of the existing methods. Therefore, ensemble learning can be applied to anomaly detection in Bitcoin by combining multiple ML classifiers. In the proposed model, the ADASYN-TL (Adaptive Synthetic + Tomek Link) balancing technique is used for data balancing. Random search, grid search and Bayesian optimization are used for hyperparameter tuning. The hyperparameters have a great impact on the performance of the model. For classification, we used the stacking model by combining Decision Tree, Naive Bayes, K-Nearest Neighbors, and Random Forest. We used SHapley Additive exPlanation (SHAP) to interpret the predictions of the stacking model. The model also explores the performance of different classifiers using accuracy, F1-score, Area Under Curve-Receiver Operating Characteristic (AUC-ROC), precision, recall, False Positive Rate (FPR) and execution time, and ultimately selects the ideal model. The proposed model contributes to the development of effective fraud detection models that address the limitations of the existing algorithms. Our stacking model, which combines the prediction of multiple classifiers, achieved the highest F1-score of 97%, precision of 96%, recall of 98%, accuracy of 97%, AUC-ROC of 99% and FPR of 3%.

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“…Ref. [18] used supervised learning methods to classify malicious and nonmalicious addresses in Ethereum and found that linear and non-linear machine learning methods outperformed integrated learning methods for address classification. These studies do not separate the detection of various malicious activities.…”

Section: Graph Embeddingmentioning

confidence: 99%

Graph Embedding-Based Money Laundering Detection for Ethereum

et al. 2023

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The number of money laundering crimes for Ethereum and the amount involved have grown exponentially in recent years. However, previous studies related to anomaly detection for Ethereum usually consider multiple types of financial crimes as a whole, ignoring the apparent differences between money laundering and other malicious activities and lacking a more granular detection targeting money laundering. In this paper, for the first time, we propose an improved graph embedding algorithm specifically for money laundering detection called GTN2vec. By mining Ethereum transaction records, the algorithm comprehensively considers the behavioral patterns of money launderers and structural information of transaction networks and can automatically extract features of money laundering addresses. Specifically, we fuse the gas price and timestamp from the transaction records into a new weight and set appropriate return and exploration parameters to modulate the sampling tendency of random walk to characterize the money laundering nodes. We construct the dataset using real Ethereum data and evaluate the effectiveness of GTN2vec on the dataset by various classifiers such as random forest. The experimental results show that GTN2vec can accurately and effectively extract money laundering account features and significantly outperform other advanced graph embedding methods.

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Classifying Transactional Addresses using Supervised Learning Approaches over Ethereum Blockchain

Cited by 8 publications

References 12 publications

K-Means Clustering and Multilayer Perceptron for Categorizing Student Business Groups

K-Means Clustering and Multilayer Perceptron for Categorizing Student Business Groups

A New Framework for Fraud Detection in Bitcoin Transactions Through Ensemble Stacking Model in Smart Cities

Graph Embedding-Based Money Laundering Detection for Ethereum

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