Comparative Study of Machine Learning Algorithms for Fraud Detection in Blockchain

Bhowmik, Madhuparna; Chandana, Tulasi Sai Siri; Rudra, Bhawana

doi:10.1109/iccmc51019.2021.9418470

Cited by 20 publications

(11 citation statements)

References 11 publications

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“…Additionally, NoSQL databases were used to store the data retrieved from the blockchain for easier processing [81], while Drungilas et al [101] tested whether it was better to keep all the data on the chaincode, or to combine the chaincode with the Oracle web service. Another way to store data is using a CSV file [127,149,153] because of its simplicity and ease of further processing.…”

Section: Discussionmentioning

confidence: 99%

“…Peak values can be eliminated by subtracting the column mean and dividing it with the standard deviation [42]. Data can be further normalised [51,105,127,146] and, when working with smart contracts, the operators and operands can be removed from the opcodes [63,117,131]. When working with transactions, if needed, the data can be grouped by user or address [42,77,111,153].…”

Section: Discussionmentioning

confidence: 99%

“…Bhowmik et al [127] presented a comparative study of machine learning algorithms used for fraud detection in blockchain networks. They used the node2vec algorithm to collect data for the data set.…”

Section: Financial Fraud Detectionmentioning

confidence: 99%

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Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

2021

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Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing an important role in the modern Information Technology (IT) revolution. Blockchain has recently been popularized by the rise of cryptocurrencies, while data mining has already been present in IT for many decades. Data stored in a blockchain can also be considered to be big data, whereas data mining methods can be applied to extract knowledge hidden in the blockchain. In a nutshell, this paper presents the interplay of these two research areas. In this paper, we surveyed approaches for the data mining of blockchain data, yet show several real-world applications. Special attention was paid to anomaly detection and fraud detection, which were identified as the most prolific applications of applying data mining methods on blockchain data. The paper concludes with challenges for future investigations of this research area.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

2021

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“…The authors [78] gives a comprehensive evaluation of different supervised ML algorithms, such as bagging models (RF), boosting models (AdaBoost), and others, to prevent fraud. This research concluded that utilizing AdaBoost and RF classifier produced the best performance result among the other seven algorithms.…”

Section: A An Anomaly In the Aspect Of Cybercrimementioning

confidence: 99%

Combining Multiple Classifiers using Ensemble Method for Anomaly Detection in Blockchain Networks: A Comprehensive Review

Hisham¹,

Makhtar²,

Aziz³

2022

IJACSA

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Blockchain is one of the most anticipated technology revolutions, with immense promise in various applications. It is a distributed and encrypted database that can address a range of challenges connected to online security and trust. While many people identify Blockchain with cryptocurrencies such as Bitcoin, it has a wide range of applications in supply chain management, health, Internet of Things (IoT), education, identity theft prevention, logistics, and the execution of digital smart contracts. Although Blockchain Technology (BT) has numerous advantages for Decentralized Applications (DApps), it is nevertheless vulnerable to abuse, smart contract failures, security, theft, trespassing, and other concerns. As a result, using Machine Learning (ML) models to detect anomalies is an excellent way to detect and safeguard blockchain networks from criminal activity. Adapting ensemble learning methods in ML to create better prediction outcomes is a viable approach for anomaly identification. Ensemble learning, as the name implies, refers to creating a stronger and more accurate classification by combining the prediction results of numerous weak models. As a result, an in-depth evaluation of ensemble learning methodologies for anomaly detection in the blockchain network ecosystem is applied in this paper. It comprises numerous ensemble methods (e.g., averaging, voting, stacking, boosting, bagging). The review collects data from three established databases, which are Scopus, Web of Science (WoS), and Google Scholar. Specific keywords are employed, such as Blockchain, Ethereum, Bitcoin, Anomaly Detection, and Ensemble Learning, employing advanced searching algorithms. The results of the search found 60 primary articles from 2017 to 2022 (30 from Scopus, 20 from the WoS, and 10 from Google Scholar). Based on these findings, we decided to divide our debate into three primary themes: (1) the fundamentals of Blockchain Technology (BT), (2) the overview of ensemble learning, and (3) the integration and analysis of ensemble learning in blockchain networks for anomaly detection. In terms of awareness and knowledge, the results are also discussed in terms of what they mean and where future research should go.

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“…It was observed that the RF algorithm achieved the best results with a detection precision of 85.71. In another study, eight different supervised machine learning techniques were presented and analyzed by Bhowmik et al [26] to investigate illegal transactions on the blockchain network. These include Naive Bayes (NB), LR, MLP, SVM, RF, Ada Boost, etc.…”

Section: Introductionmentioning

confidence: 99%

Money Laundering Detection using Machine Learning and Deep Learning

Alotibi¹,

Almutanni²,

Alsubait³

et al. 2022

IJACSA

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In recent years, money laundering activities have shown rapid progress and have indeed become the main concern for governments and financial institutions all over the world. As per recent statistics, $800 billion to $2 trillion is the estimated value of money laundered annually, in which $5 billion of the total is obtained from cryptocurrency money laundering. As per the financial action task force (FATF), the criminals may trade illegally obtained fiat money for the cryptocurrency. Accordingly, detecting and preventing illegal transactions becomes a serious threat to governments and it has been indeed challenging. To combat money laundering, especially in cryptocurrency, effective techniques for detecting suspicious transactions must be developed since the current preventive efforts are outdated. In fact, deep learning and machine learning techniques may provide novel methods to detect suspect currency movements. This study investigates the applicability of deep learning and machine learning techniques for anti-money laundering in cryptocurrency. The techniques employed in this study are Deep Neural Network (DNN), random forest (RF), K-Nearest Neighbors(KNN), and Naive Bayes (NB) with the bitcoin elliptic dataset. It was observed that the DNN and random forest classifier have achieved the highest accuracy rate with promising findings in decreasing the false positives as compared to the other classifiers. In particular, the random forest classifier outperforms DNN and achieves an F1-score of 0.99%.

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Comparative Study of Machine Learning Algorithms for Fraud Detection in Blockchain

Cited by 20 publications

References 11 publications

Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

Combining Multiple Classifiers using Ensemble Method for Anomaly Detection in Blockchain Networks: A Comprehensive Review

Money Laundering Detection using Machine Learning and Deep Learning

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