Evaluating Machine-Learning Techniques for Detecting Smart Ponzi Schemes

Ibba, G.; Pierro, Giuseppe Antonio; Francesco, Marco Di

doi:10.1109/wetseb52558.2021.00012

Cited by 19 publications

(3 citation statements)

References 12 publications

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“…The scam's foundation of the Ponzi scheme is deceit and lies, and it relies on both to deceive innocent investors by encouraging their conviction that they will receive the anticipated rewards 20 . The operator of the Ponzi scheme makes use of the Ethereum platform to achieve their goal 16,21,22 . Ethereum is a decentralized blockchain platform that creates a peer-to-peer network for securely executing and verifying application code known as smart contracts.…”

Section: Related Workmentioning

confidence: 99%

Detection of Ponzi scheme on Ethereum using machine learning algorithms

Onu,

Omolara,

Alawida

et al. 2023

Sci Rep

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Security threats posed by Ponzi schemes present a considerably higher risk compared to many other online crimes. These fraudulent online businesses, including Ponzi schemes, have witnessed rapid growth and emerged as major threats in societies like Nigeria, particularly due to the high poverty rate. Many individuals have fallen victim to these scams, resulting in significant financial losses. Despite efforts to detect Ponzi schemes using various methods, including machine learning (ML), current techniques still face challenges, such as deficient datasets, reliance on transaction records, and limited accuracy. To address the negative impact of Ponzi schemes, this paper proposes a novel approach focusing on detecting Ponzi schemes on Ethereum using ML algorithms like random forest (RF), neural network (NN), and K-nearest neighbor (KNN). Over 20,000 datasets related to Ethereum transaction networks were gathered from Kaggle and preprocessed for training the ML models. After evaluating and comparing the three models, RF demonstrated the best performance with an accuracy of 0.94, a class-score of 0.8833, and an overall-score of 0.96667. Comparative evaluations with previous models indicate that our model achieves high accuracy. Moreover, this innovative work successfully detects key fraud features within the Ponzi scheme dataset, reducing the number of features from 70 to only 10 while maintaining a high level of accuracy. The main strength of this proposed method lies in its ability to detect clever Ponzi schemes from their inception, offering valuable insights to combat these financial threats effectively.

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Section: Related Workmentioning

confidence: 99%

Detection of Ponzi scheme on Ethereum using machine learning algorithms

Onu,

Omolara,

Alawida

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Ostapowicz and Żbikowski (2019) have compared the capabilities of Support Vector Machines, Random Forest and XGBoost to identify malicious accounts. In Ibba et al (2021), machine learning algorithms are used to classify the smart contracts that are a part of a Ponzi Scheme. The dataset used for training the model consisted of the Ponzi schemes that existed in the Ethereum platform in the years between 2016 and 2018.…”

Section: Literature Surveymentioning

confidence: 99%

A novel approach to detect fraud in Ethereum transactions using stacking

et al. 2023

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Ever since Ether is launched as a digital currency, its rise has been rapid. It is currently the second most valuable digital currency in the world. There are more than 1 million transactions happening on the Ethereum network every day, and this number is expected to continue to increase. Due to the increasing number of transactions, fraudulent transactions have also increased, which has resulted in a large amount of money being lost and has also destroyed the livelihoods of many individuals. Due to their similarity to valid transactions, it is extremely difficult to distinguish between them. Additionally, Ethereum's pseudo‐anonymity adds to the difficulty of identifying the parties involved. Since there are millions of transactions every day, it would be difficult to manually verify each one. Therefore, a mechanism for validating these transactions is needed. In this context, this paper proposes a novel approach to detecting fraudulent accounts associated with these transactions by implementing machine learning algorithms among the given set of transactions. We propose a framework for creating a stacking classifier by combining several standalone classification algorithms and creating a meta‐learner based on the output of each base algorithm. The algorithms include Logistic Regression, Naive Bayes, Decision Trees, Random Forests, AdaBoosts, KNNs, SVMs, and Gradient Boosts. As a result of combining these algorithms, a powerful classifier with the ability to detect fraudulent transactions. A variety of machine learning models were trained and evaluated on the test set using various metrics. Based on the results of the individual algorithm the Random Forest algorithm achieved the highest accuracy of 95.47%, followed by Gradient Boosting at 94.61% which is an ensemble algorithm using the boosting technique. The Stacking classifier that combines Multinomial Naive Bayes and Random Forest as the base learners and logistic regression as the Meta learner achieved the highest accuracy of 97.18% with an F1 score of 97.02%. Based on the results of all the stacking models developed, it is concluded that algorithms tend to perform better when combined properly. When compared to the other approaches, the proposed approach has outperformed the others, making it feasible in the real world to detect fraudulent transactions.

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“…Boshmaf et al [8] analyse Bitcoin address transactions, found within conversations on Bitcointalk, that are linked to a specific Ponzi scheme. Ibba et al [24] analyse Ethereum smart contracts and use statistical methods to classify 'Ponzi scheme contracts'.…”

Section: Cryptocurrency-related Scamsmentioning

confidence: 99%

“Get a higher return on your savings!”: Comparing adverts for cryptocurrency investment scams across platforms

Siu,

Hutchings

2023

2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

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This work compares machine learning methods using supervised, semi-supervised and unsupervised learning, to classify advertisements for cryptocurrency related investment scams found in the online forum Bitcointalk, and the social media platform Reddit. We extract more than 24.2 million posts from Bitcointalk and use Reddit's API to collect 2,108 submissions. We train and compare several multiclass text classification approaches and use the models with highest accuracy and F-measure to identify cryptocurrency investment scam advertisements found on both platforms. We discover around five percent of all posts collected on both sites are potential scams. We then use another text classifier to identify the scam actors involved in these investment scam advertisements. We also discover the lures used within these fraudulent adverts and find the main differences in luring techniques used between Bitcointalk and Reddit. We identify that the most prevalent lure type uses the financial principle, followed by the distraction principle in Bitcointalk, and by the authority principle in Reddit. Finally, we use subreddits as communities' proxies and compare scam advertisements within them to identify whether pensioners are being specifically targeted by cryptocurrency scam adverts. Our results show that retirement subreddits do not contain a higher number of cryptocurrency investment scam adverts compared to other investment focused subreddits.

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Evaluating Machine-Learning Techniques for Detecting Smart Ponzi Schemes

Cited by 19 publications

References 12 publications

Detection of Ponzi scheme on Ethereum using machine learning algorithms

Detection of Ponzi scheme on Ethereum using machine learning algorithms

A novel approach to detect fraud in Ethereum transactions using stacking

“Get a higher return on your savings!”: Comparing adverts for cryptocurrency investment scams across platforms

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