Ethereum Merge Scams Exposed: A Look into Ethereum Fraud

Ethereum fraud has been on the rise, with scams targeting unsuspecting investors who are eager to capitalize on the Merge. In fact, the Merge has created a perfect storm for scammers to exploit, with many fake websites and social media accounts popping up to lure victims into investing in non-existent Ethereum-based projects.

One common tactic used by scammers is to create fake websites that mimic legitimate Ethereum websites, complete with fake news and updates about the Merge. For example, a scammer may create a website that claims to offer exclusive access to a new Ethereum-based token, but in reality, it's just a phishing site designed to steal users' login credentials and personal data.

The Ethereum community has been working hard to expose these scams and protect its members from falling victim to them. In fact, the Ethereum Foundation has issued warnings about several fake websites and social media accounts that have been used to scam investors.

Ponzi schemes are a type of investment scam where returns are paid to existing investors from funds contributed by new investors, rather than from profit earned.

This unsustainable business model relies on continuous recruitment of new investors to provide returns to earlier investors, rather than generating revenue through legitimate investments.

In a Ponzi scheme, the promoter promises unusually high returns or dividends to lure people into investing, often with the promise of quick profits or guaranteed returns.

Ponzi schemes often masquerade as legitimate investment opportunities, but they rely on recruiting new victims to pay returns to earlier investors. This was the case with Merge scams, which pretended to offer a way to "upgrade" to the new Ethereum blockchain.

The Merge scams typically promised victims they would receive double their initial payment in return for sending cryptocurrency to a scammer's address, with some scams even impersonating celebrities to gain credibility.

To identify a Ponzi scheme, look for promises of unusually high returns with little to no risk, such as the Merge scams' promise of doubling one's investment.

The data from the Merge scams shows that they had an 83% success rate on September 15, the day of the Merge, and a 100% success rate on several other days before and after the Merge. This suggests that scammers were able to take advantage of a lack of understanding about the Merge to fleece unsuspecting users.

Here are some common characteristics of Ponzi schemes:

In the case of the Merge scams, the scammers told victims to send cryptocurrency to an address in order to receive double their payment, often with a sense of urgency to act quickly.

On September 15, Merge scams collected over $905,000 worth of Ether, making up eight of the top ten Ether scams.

Most Merge scams targeted a limited area, with a plurality receiving payments from users in just one country.

The United States and India were among the countries most affected by Merge scams, engaging heavily with both Merge and non-Merge scams.

Finland engaged with Merge scams at a surprisingly high level, but nearly all of that engagement was driven by three scams that only targeted Finland.

Merge scams were more likely than non-Merge scams to target users in countries with higher GDPs, by a small but noticeable margin.

A total of 123 users sent cryptocurrency to the scam at the top of the ranking on September 15, receiving $307,640 in return.

Here are the top 5 Merge scams by value received on September 15:

Merge scams had an 83% success rate on September 15, the day of the merge, and a 100% success rate on several other days before and after the Merge.

Researchers have proposed a machine learning classification method to detect Ponzi schemes on Ethereum, which involves training models using supervised learning techniques. This approach uses three machine learning algorithms: KNN, RF, and NN, which can handle complex relationships between input features and target.

The detection model was trained using a tenfold cross-validation method, which means the dataset was split into 10 consecutive folds, and each fold was used as a validator once while the remaining nine folds served as the training set.

The proposed method aims to enhance Ponzi scheme detection on Ethereum through novel ML algorithms, which can help prevent financial damage and protect potential victims. Experimental results show the detection and mitigation of security threats posed by Ponzi schemes, which carry higher risk compared to many online crimes.

Here are some key metrics used to evaluate the performance of the models:

The classification and detection/prediction of Ponzi schemes on Ethereum is a crucial step in preventing financial damage and protecting potential victims. The proposed Ponzi scheme detection method was a machine learning classification, a supervised learning technique, which involves training a model to classify each transaction as either a Ponzi scheme or not Ponzi.

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Three machine learning algorithms were applied during the model training: KNN, RF, and NN algorithms, which are capable of handling both small and large datasets and complex relationships between the input features and target. These algorithms were chosen for their ability to accurately predict the likelihood of a transaction being Ponzi.

The models were evaluated for their performance using metrics such as accuracy, precision, recall, Kappa, and F1 score. The dataset was split into 10 consecutive folds, with each fold used as a validator once, while the remaining nine folds served as the training set.

A comparison of feature performance is presented in Table 4, which shows that the opcode features are quite efficient in detecting smart Ponzi schemes. The model's performance can be increased by integrating opcode and account features.

The most important features in the model's training were 'maxTimeBetweenRecTnx', 'avgValSent', and 'activityDays'. These features have a huge impact on the final decision, with 'maxTimeBetweenRecTnx' being the most important feature, as it indicates the total time taken to receive any transaction made.

Here are the metrics used to evaluate the models' performance:

These metrics demonstrate the effectiveness of the proposed Ponzi scheme detection method in identifying and preventing financial damage.

The Peraire-Bueno brothers discovered a vulnerability in an open source component of a common tool, known as a MEV-Boost relay. This vulnerability allowed them to steal funds by exploiting a weakness in the relay's code.

The attack was possible because the relay revealed block bodies to the proposer, so long as the proposer correctly signed a block header. However, the relay did not check if the block header was valid.

The Peraire-Bueno brothers' exploit is an incredibly innovative, technically sophisticated attack. It represents the first time a bad actor has managed to abuse the MEV system widely used by Ethereum block builders in this way and to this degree.

The attack exploited a vulnerability in the relay's code, which caused the relay to send private transactions to the block builder when it signed a block with invalid headers. This vulnerability was promptly addressed, mitigating the risk of similar attacks unless other vulnerabilities are identified.

The Peraire-Bueno brothers' actions were explicitly illegal due to their direct and unauthorized exploitation of vulnerabilities to steal funds.

To evaluate the effectiveness of a Ponzi scheme contract detection algorithm, we use four key metrics: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). TP stands for the number of correctly identified Ponzi scheme contracts, TN for correctly identified contracts without Ponzi schemes.

The performance of the Ethereum fraud detection model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. These metrics provide a clear and comprehensive view of the model's performance.

Precision, recall, and F-score are commonly used metrics for evaluating the performance of a model. Precision measures the number of correctly identified Ponzi scheme contracts, recall measures the number of correctly identified Ponzi scheme contracts out of all actual Ponzi scheme contracts, and F-score is the harmonic mean of precision and recall.

The performance of the Ethereum fraud detection model was measured using a range of metrics, including accuracy, precision, recall, and F1-score.

These metrics provide a clear and comprehensive view of the model's performance, allowing for comparisons with other models and algorithms.

The model's performance was evaluated using precision, recall, and F-score, which are calculated based on the number of correctly identified Ponzi scheme contracts (TP), incorrectly predicted Ponzi scheme contracts (FN), and smart contracts without Ponzi schemes that are incorrectly predicted as Ponzi scheme contracts (FP).

Precision is the ratio of TP to the sum of TP and FP, while recall is the ratio of TP to the sum of TP and FN.

The F1-score, which takes into account both precision and recall, was also used to evaluate the model's performance.

The feature extraction process was another metric used for evaluation, providing a clear understanding of the model's strengths and weaknesses.

Feature performance is a crucial aspect of detecting smart Ponzi schemes. The detection of Smart Ponzi schemes using the three types of characteristics shows that opcode features are quite efficient.

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The account features surprisingly, are ineffective at detecting clever Ponzi schemes. This is because intelligent Ponzi schemes operate differently, making it difficult for the model to accurately predict their likelihood.

The relevant measurements demonstrate that models based just on opcode properties can be used to detect smart Ponzi schemes. This is a significant finding, as it highlights the importance of opcode features in detecting these types of schemes.

The extracted features were selected based on their importance in the model's training, and they were plotted as a graph. The graph shows that 'maxTimeBetweenRecTnx', 'avgValSent', and 'activityDays' have a huge impact on the final decision.

The 'maxTimeBetweenRecTnx' feature is particularly important because it measures the total time taken to receive any transaction made. This is a key indicator of the scheme's activity level.

The 'avgValSent' feature measures the average value sent to participants within a given period. This feature is also important because it can indicate the scheme's profitability.

The 'activityDays' feature measures the number of days the Ponzi scheme operated before its collapse. This feature is a critical indicator of the scheme's longevity.