Tiffany Tien Nee Pragasam, John V. Thomas, V. Maria Anu, Subhashini Radhakrishnan
Cryptocurrencies are rapidly growing and are increasingly accepted by major commercial vendors. However, along with their rising popularity, they have also become the go-to currency for illicit activities driven by the anonymity they provide. Cryptocurrencies such as the one on the Ethereum blockchain provide a way for entities to hide their real-world identities behind pseudonyms, also known as addresses. Hence, the purpose of this work is to uncover the level of anonymity in Ethereum by investigating multiclass classification models for Externally Owned Accounts (EOAs) of Ethereum. The researchers aim to achieve this by examining patterns of transaction activity associated with these addresses. Using a labelled Ethereum address dataset from Kaggle and the Ethereum crypto dataset by Google BigQuery, an address profiles dataset was compiled based on the transaction history of the addresses. The compiled dataset, consisting of 4371 samples, was used to tune and evaluate the Random Forest, Gradient Boosting and XGBoost classifier for predicting the category of the addresses. The best-performing model found for the problem was the XGBoost classifier, achieving an accuracy of 75.3% with a macro-averaged F1-Score of 0.689. Following closely was the Random Forest classifier, with an accuracy of 73.7% and a macro-averaged F1-Score of 0.641. Gradient Boosting came in last with 73% accuracy and a macro-averaged F1-Score of 0.659. Owing to the data limitations in this study, the overall scores of the best model were weaker in comparison to similar research, with the exception of precision, which scored slightly higher. Nevertheless, the results proved that it is possible to predict the category of an Ethereum wallet address such as Phish/Hack, Scamming, Exchange and ICO wallets based on its transaction behaviour.
Along with the rapid technological and commercial innovation on the e-commerce platforms, there are an increasing number of frauds that bring great harm to these platforms. Many frauds are conducted by organized groups of fraudsters for higher efficiency and lower costs, which are also known as group-based frauds. Despite the high concealment and strong destructiveness of group-based fraud, there is no existing research work that can thoroughly exploit the information within the transaction networks of e-commerce platforms for group-based fraud detection. In this work, we analyze and summarize the characteristics of group-based frauds, based on which we propose a novel end-to-end semi-supervised Group-based Fraud Detection Network (GFDN) to support such fraud detection in real-world applications. Experimental results on large-scale e-commerce datasets from Taobao and Bitcoin trading datasets show the superior effectiveness and efficiency of our proposed model for group-based fraud detection on bipartite graphs.
In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep learning framework and incorporating more characteristics of Bitcoin transactions. We produced a dataset containing 46,045 Bitcoin transaction entities and 319,311 Bitcoin wallet addresses associated with them. We aggregated this information to form a heterogeneous graph dataset and propose three metapath representations around transaction entities, which enrich the characteristics of Bitcoin transactions. Then, we designed a metapath encoder and integrated it into a heterogeneous graph node embedding method. The experimental results indicate that our proposed framework significantly improves the accuracy of illicit Bitcoin transaction recognition compared with traditional methods. Therefore, our proposed framework is more conducive in detecting money laundering activities in Bitcoin transactions.
Mohammed A. Mohammed, Manel Boujelben, Mohamed Abid
Recently, the advent of blockchain (BC) has sparked a digital revolution in different fields, such as finance, healthcare, and supply chain. It is used by smart healthcare systems to provide transparency and control for personal medical records. However, BC and healthcare integration still face many challenges, such as storing patient data and privacy and security issues. In the context of security, new attacks target different parts of the BC network, such as nodes, consensus algorithms, Smart Contracts (SC), and wallets. Fraudulent data insertion can have serious consequences on the integrity and reliability of the BC, as it can compromise the trustworthiness of the information stored on it and lead to incorrect or misleading transactions. Detecting and preventing fraudulent data insertion is crucial for maintaining the credibility of the BC as a secure and transparent system for recording and verifying transactions. SCs control the transfer of assets, which is why they may be subject to several adverbial attacks. Therefore, many efforts have been proposed to detect vulnerabilities and attacks in the SCs, such as utilizing programming tools. However, their proposals are inadequate against the newly emerging vulnerabilities and attacks. Artificial Intelligence technology is robust in analyzing and detecting new attacks in every part of the BC network. Therefore, this article proposes a system architecture for detecting fraudulent transactions and attacks in the BC network based on Machine Learning (ML). It is composed of two stages: (1) Using ML to check medical data from sensors and block abnormal data from entering the blockchain network. (2) Using the same ML to check transactions in the blockchain, storing normal transactions, and marking abnormal ones as novel attacks in the attacks database. To build our system, we utilized two datasets and six machine learning algorithms (Logistic Regression, Decision Tree, KNN, Naive Bayes, SVM, and Random Forest). The results demonstrate that the Random Forest algorithm outperformed others by achieving the highest accuracy, execution time, and scalability. Thereby, it was considered the best solution among the rest of the algorithms for tackling the research problem. Moreover, the security analysis of the proposed system proves its robustness against several attacks which threaten the functioning of the blockchain-based healthcare application.
M. K. Ghosh, Dyuti Ghosh, Raju Halder, Joydeep Chandra
The recent surge of Ethereum in prominence has made it an attractive target for various kinds of crypto-crime. Phishing scams, for example, are an increasingly prevalent cybercrime in which malicious users attempt to steal funds from a user's crypto wallet. This research investigates the effects of network architectural features as well as the temporal aspects of user activities on the performance of detecting phishing users on the Ethereum transaction network. We employ traditional machine learning algorithms to evaluate our model on real-world Ethereum transaction data. The experimental results demonstrate that our proposed features identify phishing accounts efficiently and outperform the baseline models by 4% in Recall, and 5% in F1-score.
Credit scoring is a critical function in the banking industry, helping to assess borrowers’ creditworthiness and mitigate lending risks. Traditional credit scoring systems based on centralized storage have limitations in terms of transparency, security, and susceptibility to manipulation. This paper proposes different approaches to credit scoring that combines the strengths of various machine learning algorithms, including logistic regression, XGBoost, LightGBM, AdaBoost and RGF. Additionally, it explores the use of blockchain technology and decentralized finance (DeFi) systems to enhance the security and decentralization of the credit rating system. The study utilizes a blockchain dataset sourced from Aave’s smart contracts and employs cross-validation and ensemble modeling techniques to evaluate the performance of the models. The results demonstrate the effectiveness of the proposed approach, with the Random Forest model achieving the highest accuracy in predicting credit scores. This has the ability for improving access to credit and enhancing trust and transparency in lending decisions.
In recent years, the cryptocurrency market has been booming with an ever-increasing market capitalization. However, due to the anonymity of blockchain technology, this market has become a hotbed of financial crimes. As the largest blockchain platform supporting smart contracts, financial crimes including scams and hacking frequently happen on Ethereum and have caused serious losses. Therefore, it is necessary to classify Ethereum accounts in order to better identify those involved in illegal transactions and analyze the behavior patterns of different classes of accounts. In this paper, we construct an Ethereum transaction network based on transaction records and find that this network is with heterophily. However, most of the current work on account classification ignores the role of this heterophily information. We first figure out that the heterophily information of the neighborhood may also be beneficial for the final predictions. Based on this, we propose a new graph neural network (GNN) model, named BPA-GNN, which incorporates both homophilic and heterophilic information into the neighborhood aggregations. Specifically, BPA-GNN consists of three main modules including bi-path neighbor sampling, separated neighborhood aggregation, and attention-based node representation learning. Comprehensive experiments on a real Ethereum transaction dataset demonstrate the state-of-the-art performance of BPA-GNN, showing that the model can effectively extract and utilize neighborhood information to improve the distinguishability of node representations. As an effective solution for Ethereum account de-anonymization, BPA-GNN can help identify illegal activities and promote the healthy development of the Ethereum ecosystem.
To rationalize and automate public civil engineering works, it is crucial to directly utilize the information produced by the contractor for quality/ as-built inspection, and progress measurement.In this study, a highly reliable common data environment that utilizes blockchain and smart contracts to ensure tamper resistance and traceability of construction management information on quality and progress was developed and proved through verification tests in two project sites.
Abstract Cryptocurrencies continue to captivate businesses and investors despite market fluctuations. The number of crypto users has risen rapidly in the last few years, and alarmingly, many appear to be unaware of the risks involved. These risks aren't confined to market hazards but include very sophisticated cybercrimes related to cryptocurrencies. As cryptocurrencies have become a breeding ground for a variety of cybercrimes, resulting in enormous financial losses, it hinders user adoption limiting the utility of blockchain technology. It has become crucial to spot such scams and devise intelligent techniques to make this technology safer for investors. This paper proposes a classification model based on the Cross Industry Standard Process for Data Mining (CRISP‐DM) framework to identify fraudulent transactions over the Ethereum blockchain. Its contribution is multi‐faceted; first, the available imbalanced Ethereum dataset has been balanced to enhance the accuracy of the classification model. Second, the correlation‐based feature selection technique has been applied to retain the best discriminating features. Thirdly, an effective machine learning‐based ensemble classification model has been adopted for the identification of fraudulent transactions over the Ethereum network. A comparative analysis of 10 machine learning techniques has been presented consisting of both individual and ensemble classifiers. Evaluated outcomes show that ensemble classifiers appear to yield better performance measures over individual classifiers, and among all, the LightGbm classifier outperformed with 99.2% accuracy. Further, extensive experiments indicate that the proposed method outperforms the state‐of‐the‐art method when applied to a similar dataset.
Vaishali Gaikwad, Kunal Meher, Ryan Dass, Athisaya Sarah Jonista · 6 authors
In the 21st century financial fraud is on the rise in many institutions. Newly released Federal Trade Commission data shows that consumers reported losing nearly $8.8 billion to fraud in 2022, an increase of more than 30 percent over the previous year. The main goal for us is to develop an efficient fraud detection system and utilize blockchain to create a decentralized banking application. Our team has collaborated on various cutting-edge technologies, such as machine learning and blockchain, to create a sophisticated fraud detection system. We have implemented three machine learning algorithms, namely Logistic Regression, Decision Tree, and Random Forest, which have been used to improve the accuracy of the model for detecting fraudulent activities. As for fraud aversion, we have used the Blockchain technology which provides a tamper-proof system that can securely record and track financial transactions, ensuring transparency and security. This feature makes it an ideal solution for fraud prevention, as it guarantees that all transactions are legitimate and free from any manipulations. By combining the power of machine learning and blockchain technology, our team is confident in providing an innovative solution that will benefit all stakeholders involved
Mr. Amit Chugh, Sharad Rana, Somya Banga, Ujjwal Sharma · 5 authors
Abstract: Verifying skills is one of the major challenges in hiring new personnel. Companies and academia occasionally struggle to identify a candidate's talents because the credentials abilities that a candidate claims are not immediately verifiable and expensive for examine. For tamper-proof data storage, Blockchains have been proposed in the literature and decentralized talent verification. However, the majority of these schemes center on storing credentials are issued through regular universities for the blockchain. From several solutions that take system of certification itself into consideration, issues like numerous times, issues like (a) scaling with a small staff, (b) homogeneity of scores across numerous evaluators, or (c) extracting sincere effort from the evaluators are unaddressed. We suggest SkillVio, a blockchain-based platform that takes into account the above listed problems. and provide a number of enticing traits. As payment from platform users, such as test takers and employers, the network rewards graders with tokens.
Abstract With the development of blockchain, cryptocurrencies are also showing a boom. However, due to the decentralized and anonymous nature of blockchain, cryptocurrencies have inevitably become a hotbed for fraudulent crimes. For example, phishing scams are frequent, which not only jeopardize the financial security of blockchain, but also hinder the promotion of blockchain technology. To solve this problem, this paper proposes a graph neural network‐based phishing detection method for Ethereum, and validates it using Ethereum datasets. Specifically, this paper proposes a feature learning algorithm named TransWalk, which consists of a random walk strategy for transaction networks and a multi‐scale feature extraction method for Ethereum. Then, an Ethereum phishing fraud detection framework is built based on TransWalk, and conduct extensive experiments on the Ethereum dataset to verify the effectiveness of this scheme in identifying Ethereum phishing detection.
In recent years, applying smart contract to Blockchain-based Service Networks (BSNs) has been considered as one of the most promising solution to boost the integration and adoption of Blockchain in big businesses. However, smart contract are especially vulnerable to attack due to poor coding. Although many existing vulnerability detection tools are restricted by rigorous rules that are defined by the experts in advance, these tools are observed to have a high false positive rate in practice. Thus we propose a vulnerability detection framework for smart contract based on the attention mechanism and data flow. The code of smart contract is transformed to a data flow according to the abstract syntax tree that is built from the code. The data flow we built with smart contract code could represent the relationships of code semantic logic. Source code, data flow, and the tags of smart contract code are used as datasets to mask processing. Then, we construct a bidirectional multi-layer transformer architecture based on the attention mechanism to train our dataset. After training, we can get the label of whether there is a vulnerability in the final smart contract. Finally, the model we proposed reaches state-of-the-art results in the practical experiments of smart contract vulnerability detection with 92.54%, 81.79%, and 86.84% in the results Accuracy, Recall, and F1score, respectively.
Zhen Zhang, Tao He, Kai Chen, Boshen Zhang · 6 authors
As the use of digital currencies, such as cryptocurrencies, increases in popularity, phishing scams and other cybercriminal activities on blockchain platforms (e.g., Ethereum) have also risen. Current methods of detecting phishing in Ethereum focus mainly on the transaction features and local network structure. However, these methods fail to account for the complexity of interactions between edges and the handling of large graphs. Additionally, these methods face significant issues due to the limited number of positive labels available. Given this, we propose a scheme that we refer to as the Bagging Multiedge Graph Convolutional Network to detect phishing scams on Ethereum. First, we extract the features from transactions and transform the complex Ethereum transaction network into three simple inter-node graphs. Then, we use graph convolution to generate node embeddings that leverage the global structural information of the inter-node graphs. Further, we apply the bagging strategy to overcome the issues of data imbalance and the Positive Unlabeled (PU) problem in transaction data. Finally, to evaluate our approach’s effectiveness, we conduct experiments using actual transaction data. The results demonstrate that our Bagging Multiedge Graph Convolutional Network (0.877 AUC) outperforms all of the baseline classification methods in detecting phishing scams on Ethereum.
Li Duan, Liu Yang, Chunhong Liu, Wei Ni · 5 authors
Digital assets involved in smart contracts are on the rise. Security vulnerabilities in smart contracts have resulted in significant losses for the blockchain community. Existing smart contract vulnerability detection techniques have been typically single-purposed and focused only on the source code or opcode of contracts. This paper presents a new smart contract vulnerability detection method, which extracts features from different levels of smart contracts to train machine learning models for effective detection of vulnerabilities. Specifically, we propose to extract 2-gram features from the opcodes of smart contracts and token features from the source code using a pre-trained CodeBERT model, thereby capturing the semantic information of smart contracts at different levels. The 2-gram and token features are separately aggregated and then fused and input into machine-learning models to mine the vulnerability features of contracts. Over 10,266 smart contracts are used to verify the proposed method. Widespread reentrancy, timestamp dependence, and transaction-ordering dependence vulnerabilities are considered. Experiments show the fused features can help significantly improve smart contract vulnerability detection compared to the single-level features. The detection accuracy is as high as 98%, 98% and 94% for the three vulnerabilities, respectively. The average detection time is 0.99 second per contract, indicating the proposed method is suitable for automatic batch detection of vulnerabilities in smart contracts.
Phishing is a widespread scam activity on Ethereum, causing huge financial losses to victims. Most existing phishing scam detection methods abstract accounts on Ethereum as nodes and transactions as edges, then use manual statistics of static node features to obtain node embedding and finally identify phishing scams through classification models. However, these methods can not dynamically learn new Ethereum transactions. Since the phishing scams finished in a short time, a method that can detect phishing scams in real-time is needed. In this paper, we propose a streaming phishing scam detection method. To achieve streaming detection and capture the dynamic changes of Ethereum transactions, we first abstract transactions into edge features instead of node features, and then design a broadcast mechanism and a storage module, which integrate historical transaction information and neighbor transaction information to strengthen the node embedding. Finally, the node embedding can be learned from the storage module and the previous node embedding. Experimental results show that our method achieves decent performance on the Ethereum phishing scam detection task.
As one of the most active blockchain platforms at present, Ethereum attracts a great deal of interest, including that of fraudsters. They exploit the anonymity of Ethereum accounts to perpetrate varieties of scams, the most common of which is phishing frauds. However, existing phishing detection work ignores the heterogeneity of Ethereum transaction edges. In fact, the activities on Ethereum include external transactions, internal transactions, and token transactions. Therefore, this paper proposes an Ethereum account phishing fraud detection method named HTSGCN. Based on heterogeneous transaction subnets, our method makes full use of the type and direction information contained in transactions. First, we collect Ethereum transaction data and construct a k-order heterogeneous subnet for each account. To aggregate the neighbor feature, we design a message propagation mechanism based on graph convolution network. Finally, we classify node representation vectors containing neighborhood and its own characteristics. Experimental results show that HTSGCN has a better effect on detecting phishing accounts than previous work which is based on homogeneous networks.
Since the inception of permissionless blockchains with Bitcoin in 2008, it became apparent that their most well-suited use case is related to making the financial system and its advantages available to everyone seamlessly without depending on any trusted intermediaries. Smart contracts across chains provide an ecosystem of decentralized finance (DeFi), where users can interact with lending pools, Automated Market Maker (AMM) exchanges, stablecoins, derivatives, etc. with a cumulative locked value which had exceeded 160B USD. While DeFi comes with high rewards, it also carries plenty of risks. Many financial crimes have occurred over the years making the early detection of malicious activity an issue of high priority. The proposed framework introduces an effective method for extracting a set of features from different chains, including the largest one, Ethereum and it is evaluated over an extensive dataset we gathered with the transactions of the most widely used DeFi protocols (23 in total, including Aave, Compound, Curve, Lido, and Yearn) based on a novel dataset in collaboration with Covalent. Different Machine Learning methods were employed, such as XGBoost and a Neural Network for identifying fraud accounts detection interacting with DeFi and we demonstrate that the introduction of novel DeFi-related features, significantly improves the evaluation results, where Accuracy, Precision, Recall, F1-score and F2-score where utilized.
Over the years with the advent of technology 4.0, there have been an increased inclination towards the blockchain technology and the cryptocurrencies. Thus it is becoming imperative to implement fraud detection techniques over the Ethereum network which nowadays is a popular platform for the developers to create smart contracts and deploy decentralized apps. Over the years the various machine learning algorithms have been growing for the process of fraud detection and in particular neural networks have shown promising results. As such in this paper, a neural-network based approach has been presented for Ethereum fraud detection and to validate the effects of the performance this proposed model has been compared with its peers. In contrast to the various models such as Logistic Regression, SVM, Gaussian Naive Bayes, K-nearest neighbour, the neural network perform the best providing an accuracy of about 97.09% which is higher than the rest. It is then seen that neural networks are relatively effective in learning complex patterns of the dataset and thus classifying the resultant transaction as genuine or fraudulent. Thus this work contribute in the development of effective solutions for fraud detection in the Ethereum and other blockchain platforms, enhancing their security and reliability.
Credit card fraud detection is a significant challenge for the financial industry, and the privacy of sensitive financial data is of utmost importance. Federated learning is a decentralized machine learning technique that can enable collaborative model training while preserving privacy. Blockchain, with its decentralized and secure nature, can further enhance the privacy and security of federated learning. This paper explores the opportunities, challenges, and future directions of blockchain-enabled federated learning for credit card fraud detection. The combination of federated learning and blockchain can provide a secure and private platform for credit card fraud detection. Blockchain-enabled federated learning offers several opportunities, including improved privacy, security, and collaboration among different financial institutions. The successful implementation of blockchain-enabled federated learning can revolutionize credit card fraud detection by providing a secure and private platform for collaborative model training. This paper emphasizes the potential of blockchain-enabled federated learning for credit card fraud detection and highlights the need to address the challenges associated with this technology. It is essential to continue exploring and developing blockchain-enabled federated learning to ensure the security and privacy of sensitive financial data while promoting collaboration and innovation in the financial industry.
Fan Jiang, Kailin Chao, Jianmao Xiao, Qinghua Liu · 7 authors
As blockchain technology continues to advance, smart contracts, a core component, have increasingly garnered widespread attention. Nevertheless, security concerns associated with smart contracts have become more prominent. Although machine-learning techniques have demonstrated potential in the field of smart-contract security detection, there is still a lack of comprehensive review studies. To address this research gap, this paper innovatively presents a comprehensive investigation of smart-contract vulnerability detection based on machine learning. First, we elucidate common types of smart-contract vulnerabilities and the background of formalized vulnerability detection tools. Subsequently, we conduct an in-depth study and analysis of machine-learning techniques. Next, we collect, screen, and comparatively analyze existing machine-learning-based smart-contract vulnerability detection tools. Finally, we summarize the findings and offer feasible insights into this domain.
Yu Gai, Liyi Zhou, Kaihua Qin, Dawn Song · 5 authors
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. Unlike traditional methods, BlockGPT is designed to offer an unrestricted search space and does not rely on predefined rules or patterns, enabling it to detect a broader range of anomalies. We demonstrate the effectiveness of BlockGPT through its use as an anomaly detection tool for Ethereum transactions. In our experiments, it effectively identifies abnormal transactions among a dataset of 68M transactions and has a batched throughput of 2284 transactions per second on average. Our results show that, BlockGPT identifies abnormal transactions by ranking 49 out of 124 attacks among the top-3 most abnormal transactions interacting with their victim contracts. This work makes contributions to the field of blockchain transaction analysis by introducing a custom data encoding compatible with the transformer architecture, a domain-specific tokenization technique, and a tree encoding method specifically crafted for the Ethereum Virtual Machine (EVM) trace representation.