Explainable Ponzi Schemes Detection on Ethereum
- URL: http://arxiv.org/abs/2301.04872v2
- Date: Thu, 18 Apr 2024 12:51:25 GMT
- Title: Explainable Ponzi Schemes Detection on Ethereum
- Authors: Letterio Galletta, Fabio Pinelli,
- Abstract summary: Ponzi schemes are one of the most common scams.
In this paper, we present a classifier for detecting smart Ponzi contracts on the real-world.
We identify a small and effective set of features that ensures a good classification quality and investigate their impacts on the classification using AI techniques.
- Score: 1.3812010983144802
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Blockchain technology has been successfully exploited for deploying new economic applications. However, it has started arousing the interest of malicious actors who deliver scams to deceive honest users and to gain economic advantages. Ponzi schemes are one of the most common scams. Here, we present a classifier for detecting smart Ponzi contracts on Ethereum, which can be used as the backbone for developing detection tools. First, we release a labelled data set with 4422 unique real-world smart contracts to address the problem of the unavailability of labelled data. Then, we show that our classifier outperforms the ones proposed in the literature when considering the AUC as a metric. Finally, we identify a small and effective set of features that ensures a good classification quality and investigate their impacts on the classification using eXplainable AI techniques.
Related papers
- Dual-view Aware Smart Contract Vulnerability Detection for Ethereum [5.002702845720439]
We propose a Dual-view Aware Smart Contract Vulnerability Detection Framework named DVDet.
The framework initially converts the source code and bytecode of smart contracts into weighted graphs and control flow sequences.
Comprehensive experiments on the dataset show that our method outperforms others in detecting vulnerabilities.
arXiv Detail & Related papers (2024-06-29T06:47:51Z) - Towards Effective Detection of Ponzi schemes on Ethereum with Contract Runtime Behavior Graph [17.79695486585971]
Ponzi schemes, a form of scam, have been discovered in smart contracts in recent years, causing massive financial losses.
Existing detection methods primarily focus on rule-based approaches and machine learning techniques.
We propose PonziGuard, an efficient Ponzi detection approach based on contract runtime behavior.
arXiv Detail & Related papers (2024-06-03T01:17:48Z) - Vulnerability Scanners for Ethereum Smart Contracts: A Large-Scale Study [44.25093111430751]
In 2023 alone, such vulnerabilities led to substantial financial losses exceeding a billion of US dollars.
Various tools have been developed to detect and mitigate vulnerabilities in smart contracts.
This study investigates the gap between the effectiveness of existing security scanners and the vulnerabilities that still persist in practice.
arXiv Detail & Related papers (2023-12-27T11:26:26Z) - Improving the Accuracy of Transaction-Based Ponzi Detection on Ethereum [13.233535179219633]
Ponzi scheme, an old-fashioned fraud, is now popular on the blockchain.
Most Ponzi detection methods detect a Ponzi scheme based on its smart contract source code.
We propose a new set of 85 features (22 known account-based and 63 new time-series features) which allows machine learning algorithms to achieve up to 30% higher F1-scores.
arXiv Detail & Related papers (2023-08-31T01:54:31Z) - Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection.
A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes.
Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
arXiv Detail & Related papers (2023-07-11T07:48:39Z) - SourceP: Detecting Ponzi Schemes on Ethereum with Source Code [0.5898893619901381]
SourceP is a method to detect smart Ponzi schemes on the platform using pre-trained models and data flow.
We first convert the source code of a smart contract into a data flow graph and then introduce a pre-trained model based on learning code representations to build a classification model.
The experimental results show that SourceP achieves 87.2% recall and 90.7% F-score for detecting smart Ponzi schemes.
arXiv Detail & Related papers (2023-06-02T16:40:42Z) - Blockchain Large Language Models [65.7726590159576]
This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions.
The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System.
arXiv Detail & Related papers (2023-04-25T11:56:18Z) - You are caught stealing my winning lottery ticket! Making a lottery
ticket claim its ownership [87.13642800792077]
Lottery ticket hypothesis (LTH) emerges as a promising framework to leverage a special sparse subnetwork.
Main resource bottleneck of LTH is however the extraordinary cost to find the sparse mask of the winning ticket.
Our setting adds a new dimension to the recently soaring interest in protecting against the intellectual property infringement of deep models.
arXiv Detail & Related papers (2021-10-30T03:38:38Z) - Data-driven Smart Ponzi Scheme Detection [11.467476506780969]
A smart Ponzi scheme is a new form of economic crime that uses smart contract account and cryptocurrency to implement Ponzi scheme.
We propose a data-driven smart Ponzi scheme detection system in this paper.
Compared with traditional methods, the proposed system requires very limited human-computer interaction.
arXiv Detail & Related papers (2021-08-20T07:45:36Z) - Smart Contract Vulnerability Detection: From Pure Neural Network to
Interpretable Graph Feature and Expert Pattern Fusion [48.744359070088166]
Conventional smart contract vulnerability detection methods heavily rely on fixed expert rules.
Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge.
We develop automatic tools to extract expert patterns from the source code.
We then cast the code into a semantic graph to extract deep graph features.
arXiv Detail & Related papers (2021-06-17T07:12:13Z) - ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep
Neural Network and Transfer Learning [80.85273827468063]
Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable.
We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts.
We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
arXiv Detail & Related papers (2021-03-23T15:04:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.