Department of Computer Science, University of Abuja, Gwagwalada, Nigeria.
Department of Computer Sciences, Abu Dhabi University, 59911, Abu Dhabi, United Arab Emirates.
Sci Rep. 2023 Oct 27;13(1):18403. doi: 10.1038/s41598-023-45275-0.
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.
庞氏骗局带来的安全威胁比许多其他在线犯罪要高得多。这些欺诈性的在线企业，包括庞氏骗局，在像尼日利亚这样的社会中迅速增长，成为主要威胁，特别是由于高贫困率。许多人成为这些骗局的受害者，导致了巨大的财务损失。尽管已经使用各种方法（包括机器学习 (ML)）来检测庞氏骗局，但当前的技术仍然面临挑战，例如数据集不足、依赖交易记录以及准确性有限。为了解决庞氏骗局的负面影响，本文提出了一种使用机器学习算法（如随机森林 (RF)、神经网络 (NN) 和 K 最近邻 (KNN)）在以太坊上检测庞氏骗局的新方法。从 Kaggle 收集了超过 20,000 个与以太坊交易网络相关的数据集，并对其进行预处理以训练 ML 模型。在评估和比较这三个模型后，RF 表现出最好的性能，准确率为 0.94，类评分 0.8833，总评分 0.96667。与以前的模型进行比较评估表明，我们的模型具有很高的准确率。此外，这项创新性工作成功地从庞氏骗局数据集中检测到关键的欺诈特征，将特征数量从 70 个减少到仅 10 个，同时保持了很高的准确性。该方法的主要优势在于能够从一开始就检测到聪明的庞氏骗局，为有效应对这些金融威胁提供了有价值的见解。
