Recent advances in the field of large language models (LLMs), particularly the ChatGPT family, have given rise to a powerful and versatile machine interlocutor, packed with knowledge and challenging our understanding of learning. This interlocutor is a double-edged sword: it can be harnessed for a wide variety of beneficial tasks, but it can also be used to cause harm. This study explores the complicated interaction between ChatGPT and the growing problem of cryptocurrency fraud. Although ChatGPT is known for its adaptability and ethical considerations when used for harmful purposes, we highlight the deep connection that may exist between ChatGPT and fraudulent actions in the volatile cryptocurrency ecosystem. Based on our categorization of cryptocurrency frauds, we show how to influence outputs, bypass ethical terms, and achieve specific fraud goals by manipulating ChatGPT prompts. Furthermore, our findings emphasize the importance of realizing that ChatGPT could be a valuable instructor even for novice fraudsters, as well as understanding and safely deploying complex language models, particularly in the context of cryptocurrency frauds. Finally, our study underlines the importance of using LLMs responsibly and ethically in the digital currency sector, identifying potential risks and resolving ethical issues. It should be noted that our work is not intended to encourage and promote fraud, but rather to raise awareness of the risks of fraud associated with the use of ChatGPT.
Tahani Baabdullah, Amani Alzahrani, Danda B. Rawat, Chunmei Liu
Increasing global credit card usage has elevated it to a preferred payment method for daily transactions, underscoring its significance in global financial cybersecurity. This paper introduces a credit card fraud detection (CCFD) system that integrates federated learning (FL) with blockchain technology. The experiment employs FL to establish a global learning model on the cloud server, which transmits initial parameters to individual local learning models on fog nodes. With three banks (fog nodes) involved, each bank trains its learning model locally, ensuring data privacy, and subsequently sends back updated parameters to the global learning model. Through the integration of FL and blockchain, our system ensures privacy preservation and data protection. We utilize three machine learning and deep neural network learning algorithms, RF, CNN, and LSTM, alongside deep optimization techniques such as ADAM, SGD, and MSGD. The SMOTE oversampling technique is also employed to balance the dataset before model training. Our proposed framework has demonstrated efficiency and effectiveness in enhancing classification performance and prediction accuracy.
João Ricardo Pereira Marques, Jo�ão Fabrício Filho, R.A. Pereira Goncalves
Este artigo apresenta uma abordagem para a criação de um dataset com os códigos dos contratos implantados na rede Ethereum. O critério de seleção compreende os três tipos de transações: regulares, de criação e de execução do código dos contratos. As ferramentas ethereumetl e Duckdb são utilizadas para extração dos dados e a criação do banco de dados. A base de dados reúne informações de blocos, transações, código dos contratos, informações sobre as instruções utilizadas em cada contrato e quantas vezes cada contrato foi executado e em quais versões da Ethereum Virtual Machine (EVM).
Language models (LMs) have demonstrated superior performance in detecting fraudulent activities on Blockchains. Nonetheless, the sheer volume of Blockchain data results in excessive memory and computational costs when training LMs from scratch, limiting their capabilities to large-scale applications. In this paper, we present ZipZap, a framework tailored to achieve both parameter and computational efficiency when training LMs on large-scale transaction data. First, with the frequency-aware compression, an LM can be compressed down to a mere 7.5% of its initial size with an imperceptible performance dip. This technique correlates the embedding dimension of an address with its occurrence frequency in the dataset, motivated by the observation that embeddings of low-frequency addresses are insufficiently trained and thus negating the need for a uniformly large dimension for knowledge representation. Second, ZipZap accelerates the speed through the asymmetric training paradigm: It performs transaction dropping and cross-layer parameter-sharing to expedite the pre-training process, while revert to the standard training paradigm for fine-tuning to strike a balance between efficiency and efficacy, motivated by the observation that the optimization goals of pre-training and fine-tuning are inconsistent. Evaluations on real-world, large-scale datasets demonstrate that ZipZap delivers notable parameter and computational efficiency improvements for training LMs. Our implementation is available at: https://github.com/git-disl/ZipZap.
Student, CSE, Sir MVIT, B Sumangala, Aman Raj, Amritanshu Bhardwaj · 6 authors
Abstract - CrypticReport is a decentralized crime reporting system designed to make public reporting safer, more transparent, and free from identity risks. Citizens often avoid reporting crimes due to fear of exposure, harassment, or data misuse. CrypticReport overcomes these challenges by combining blockchain technology, decentralized IPFS storage, artificial intelligence or zero-knowledge–based authentication. Using Anon Aadhaar, users can verify their identity without revealing any personal information. AI models classify reports to block spam and detect duplicate submissions. All verified reports and evidence are stored in IPFS, and their hashes are recorded on the blockchain for tamper-proof storage. The platform uses a React interface for reporting, a Flask backend for AI processing, Ethereum smart contracts for record immutability, and the Waku protocol for real-time updates between citizens and authorities. Testing shows that the system improves trust, preserves anonymity, and ensures that no data can be altered once submitted. CrypticReport proves that decentralized systems can make crime reporting more secure, reliable, and citizen-friendly. Key Words: Blockchain, IPFS, Anonymous Reporting, AI Classification, Zero-Knowledge Proof, Decentralized Systems
The purpose of this article is to look at recent developments in forensic accounting that have to do with preventing and investigating fraud. The following new developments in forensic accounting are being studied by doing a thorough literature review: data analytics, cyber forensic accounting, and the impact of blockchain and cryptocurrencies on the field. We take a close look at each new trend, breaking it down into its uses, pros, disadvantages, and ethical implications. Case studies and real-world examples back up the findings, showing how effective these fraud prevention and investigation tendencies are. Investigations into financial crimes employing information technology have their own set of challenges, which the report sheds light on. Blockchain technology’s capacity to increase accountability, traceability, and transparency in financial transactions is also explored. To improve fraud detection and prevention efforts, the study finishes with suggestions for researchers, practitioners, and policymakers to adapt to and take advantage of these new trends. To effectively identify and discourage financial crime in the constantly evolving world of new technology, the study finishes by stressing the necessity for continuous research and innovation, highlighting the dynamic character of forensic accounting.
Ethereum and its native cryptocurrency, Ether, have played a worthy attention in the development of the blockchain and cryptocurrency space. Its programmability and smart contract capabilities have made it a foundational platform for decentralized applications and innovations across various industries. Because of its anonymous and decentralized structure, the hotheaded expansion of cryptocurrencies in the payment space has created both enormous potential and concerns related to cybercrime, including money laundering, financing terrorism, illegal and dangerous services. As more financial institutions attempt to integrate cryptocurrencies into their networks, there is an increasing need to create a more transparent network that can withstand these kinds of attacks. In this work, we are using different classification techniques, such as logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) for Ethereum fraud detection. The dataset we are using includes rows of legitimate transactions done using the cryptocurrency Ethereum as well as known fraudulent transactions. The “XGBoost” model, which is noteworthy, detects variations that might attract notice and prevent potential issues in this chore.
Abstract: As a consequence of mass unemployment being the byproduct of COVID-19, people around the world discovered investment in cryptocurrency as a means to tackle their declining financial condition. Subsequently, the prominence of Ethereum as a platform for crypto transactions also gave rise to fraudulent transactions. The need to detect these frauds exists even today. This study proposes a token-based approach to detect fraud in Ethereum transactions incorporating the ERC20 standard, by employing machine learning techniques. After cleaning and preprocessing of the dataset, the transaction data was fed to Random Forest (RF), AdaBoost, Extra Trees (ET), Gradient Boosting (GB) and Extreme Gradient Boosting (XGB) classifiers in search of the most suitable model for fraud detection. Meticulous evaluation revealed that RF, ET and XGB classifiers yielded the highest accuracy of 95%. The proposed token-based approach hence presents a novel and efficient solution for fraud detection, with room for improvement and scalability.
Abstract: Fraudulent transactions significantly impact blockchain network trust and the economy. Traditional consensus methods (e.g., proof of work or proof of stake) can't confirm the identity of participants, leaving the network susceptible to fraud. Machine learning algorithms offer a potential solution to detect fraudulent transactions and participants. Fraudulent exchanges in the blockchain economy deter investors and raise skepticism. This study explores the effectiveness of controlled AI and deep learning models in identifying fraudulent transactions and users, integrating machine learning with blockchain technology.
The increasing complexity of fraud in large enterprises, particularly in financial and transactional ecosystems, necessitates a scalable, agile, and distributed detection approach. Traditional centralized fraud detection architectures struggle to keep pace with the real-time requirements, data silos, and domain-specific fraud patterns that arise across organizational units. This paper explores the application of Data Mesh architecture as a decentralized and domain-oriented paradigm for enhancing fraud detection in large enterprises. Data Mesh shifts the ownership of data from centralized teams to domain-specific teams, treating data as a product and enabling better scalability, autonomy, and responsiveness. We propose a Data Mesh-based fraud detection model wherein each business domain—such as sales, finance, customer relations, and operations—operates as a semi-autonomous node capable of detecting fraud patterns locally while contributing to an enterprise-wide fraud intelligence network. Leveraging a federated governance model, the architecture facilitates standardized yet decentralized policy enforcement, model deployment, and cross-domain collaboration. This paper examines how data product thinking, domain-driven design, self-serve data platforms, and federated computational governance work together to create a resilient and adaptable architecture for fraud detection. The proposed methodology utilizes distributed anomaly detection algorithms, local event-driven stream processing (e.g., Apache Kafka and Flink), and inter-domain feedback loops for continuous model retraining and behavior correlation. Experimental simulations conducted on synthetic multi-domain enterprise data reveal improved time-to-detection, reduced false positives, and enhanced fraud detection in low-signal data scenarios compared to centralized models. Furthermore, the architecture demonstrates superior scalability and flexibility when integrating new domains and updating detection logic. The findings of this research indicate that Data Mesh not only democratizes access to fraud-related data but also enhances detection capabilities by aligning technical solutions with organizational complexity. This paper contributes to the growing body of decentralized AI applications in enterprises and offers actionable design patterns for implementing domain-centric fraud analytics in large organizations. Future work includes extending this architecture to incorporate privacy-preserving technologies such as federated learning and exploring its applicability in regulatory compliance frameworks
Rodrigo Colnago Contreras, Vitor Trevelin Xavier da Silva, Igor Trevelin Xavier da Silva, Monique Simplicio Viana · 8 authors
Since financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily predictions about the movement direction of financial time series through classification models, financial time series preprocessing methods, and feature selection with genetic algorithms. The target time series are Bitcoin, Ibovespa, and Vale. The methodology of this paper includes the following steps: collecting time series of financial assets; data preprocessing; feature selection with genetic algorithms; and the training and testing of machine learning models. The results were obtained by evaluating the models with the area under the ROC curve metric. For the best prediction models for Bitcoin, Ibovespa, and Vale, values of 0.61, 0.62, and 0.58 were obtained, respectively. In conclusion, the feature selection allowed the improvement of performance in most models, and the input series in the form of percentage variation obtained a good performance, although it was composed of fewer attributes in relation to the other sets tested.
Shimal Sh. Taher, Siddeeq Y. Ameen, Jihan A. Ahmed
In recent years, cryptocurrencies have experienced rapid growth and adoption, revolutionizing the financial sector. However, the rise of digital currencies has also led to an increase in fraudulent transactions and illegal activities. In this paper, we present a comprehensive study on the detection of fraudulent transactions in the context of cryptocurrency exchanges, with a primary focus on the Ethereum network. By employing various Machine Learning (ML) techniques and ensemble methods, including the hard voting ensemble model, which achieved a remarkable 99% accuracy, we aim to effectively identify suspicious transactions while maintaining high accuracy and precision. Additionally, we delve into the importance of eXplainable Artificial Intelligence (XAI) to enhance transparency, trust, and accountability in AI-based fraud detection systems. Our research contributes to the development of reliable and interpretable models that can significantly improve the cryptocurrency ecosystem security and integrity.
Mohammad H. Hasan, Mohammad Shahriar Rahman, Helge Janicke, Iqbal H. Sarker
As the use of Blockchain for digital payments continues to rise in popularity, it also becomes susceptible to various malicious attacks. Successfully detecting anomalies within Blockchain transactions is essential for bolstering trust in digital payments. However, the task of anomaly detection in Blockchain transaction data is challenging due to the infrequent occurrence of illicit transactions. Although several studies have been conducted in the field, a limitation persists: the lack of explanations for the model's predictions. This study seeks to overcome this limitation by integrating eXplainable Artificial Intelligence (XAI) techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin transactions. The Shapley Additive exPlanation (SHAP) method is employed to measure the contribution of each feature, and it is compatible with ensemble models. Moreover, we present rules for interpreting whether a Bitcoin transaction is anomalous or not. Additionally, we have introduced an under-sampling algorithm named XGBCLUS, designed to balance anomalous and non-anomalous transaction data. This algorithm is compared against other commonly used under-sampling and over-sampling techniques. Finally, the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble classifiers. Our experimental results demonstrate that: (i) XGBCLUS enhances TPR and ROC-AUC scores compared to state-of-the-art under-sampling and over-sampling techniques, and (ii) our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy, TPR, and FPR scores.
In this paper, we introduce the concept of Reputation Oracles: programs that can predict whether a given smart contract is reputable, or malicious. Our approach to classification combines state-of-the-art deep learning techniques with one of the least-explored types of onchain data: decompiled smart contract bytecode. To get a baseline for reputation, we trained our model on thousands of smart contracts representing not only reputable applications, such as leading DeFi protocols, but also malicious contracts that implement scam functionality like rug pulls, phishing, or spoofing. Remarkably, the resulting model boasts 94% test accuracy, outperforming a naive baseline model in specificity, negative predictive value, and overall accuracy. Under the hood, the model leverages transfer learning by applying the encoder portion of a transformer for feature representation, followed by a specifically trained feed-forward neural network (FFN) layer for the classification of smart contracts. Although initially applied to reputation prediction, our findings indicate that decompiled bytecode preserves enough relevant information from the original source code to be effectively used for various other classification tasks, thereby enabling better contextualization of smart contracts and the primitives they implement.
This study introduces an interpretable imbalanced data classification method for detecting cryptocurrency transaction fraud. We address data imbalance using SMOTE oversampling and data augmentation through contrastive learning. Next, we introduce a Transformer-based deep learning model that learns sample relevance. The model undergoes pre-training with a contrastive loss and fine-tuning through Bayesian optimization to effectively extract high-dimensional, higher-order, and fraud-related features. We employ a SHAP-based interpreter along with attention scores to elucidate the role of various transaction features in fraud detection. Comparative results demonstrate the model's remarkable recall performance in identifying cryptocurrency transaction fraud. Furthermore, it achieves an excellent F1 value, striking a balance between accuracy and recall. This research not only enriches financial fraud detection but also enhances cryptocurrency transaction security, promotes market development, and contributes to economic stability and social security.
As early as 2013, Vitalik Buterin introduced Ethereum with the possibility of its widespread use (Antonopoulos, 2018). There are several applications based on the Ethereum protocol, including ERC-20 tokens, which are Ethereum-based tokens that can be created and deployed on the Ethereum network. Over 400 million transactions have been made on Ethereum since its inception. There have been a number of illegal activities related to Ethereum, including smart-Ponzi schemes, phishing, money laundering, and fraud. Detecting and predicting such attacks over blockchain can be achieved through anomaly detection for blockchain. The paper makes a number of contributions; first of all, it proposes a Random Forest Classifier, which is an effective method for detecting illicit accounts on the Ethereum network based on the testing of 8 models of four distinct types (Decision Tree, Random Forest, Gradient Boosting, and Extreme Gradient Boosting); secondly, it gives a multiple linear regression model for estimating total Ethereum transfers; and thirdly, it provides coherent and graphical representations of historical data. The software tool, KNIME is used to execute the statistical tasks. This tool uses visual nodes to do descriptive, predictive and prescriptive analytics (Berthold et al., 2009).
With the wide adoption of cryptocurrency, blockchain technologies have become the foundation of such digital currencies. However, this adoption has been accompanied by a surge in cryptocurrency fraud, causing significant losses to financial organizations and individuals. One way to mitigate these losses is to use Federated Learning (FL) techniques to detect fraudulent cryptocurrency transactions. This paper provides an overview of secure, privacy-preserving, and scalable Blockchain-based Federated Learning (BCFL) as a promising solution for slowing the exponential growth of cryptocurrency fraud. BCFL enables multiple entities to collaboratively train machine learning models for detecting fraudulent cryptocurrency transactions without sharing their private data, thus preserving privacy. However, Integrating differential privacy and Secure Multi-party computation (SMPC) models in BCFL presents an additional scalability challenge. This study provides an overview of BCFL, evaluating existing research on its security, privacy, and scalability challenges in detecting cryptocurrency fraud. The review explores existing research and various methodologies, highlighting advancements and challenges in creating effective, privacy-conscious fraud detection solutions for cryptocurrency transactions. We first discuss the current state of BCFL in fraud detection, along with its potential advantages and limitations, and then discuss the existing research gaps. In particular, this paper examines various BCFL frameworks, consensus algorithms, and block architectures, emphasizing their strengths and limitations in the context of cryptocurrency fraud detection to develop scalable and privacy-preserving solutions. We compare various solutions that address scalability and privacy challenges in BCFL, including adopting a geographically distributed cloud computing model that utilizes SMPC and lightweight consensus algorithms and protocols to manage computational overheads.
Lee Song Haw Colin, P. Mohan, Jonathan Pan, Peter K. K. Loh
Smart contract vulnerabilities have led to substantial disruptions, ranging from the DAO attack to the recent Poolz Finance. While initially, the smart contract vulnerability definition lacked standardization, even with the advancements in Solidity, the potential for deploying malicious contracts to exploit legitimate ones persists. The Abstract syntax tree (AST), opcodes, and control flow graph (CFG) are the intermediate representations for Solidity contracts. In this paper, we propose an integrated and efficient smart contract vulnerability detection algorithm based on Multi-layer perceptron (MLP). We use feature vectors from the Opcodes and CFG for the machine learning (ML) model training. The existing ML-based approaches for analyzing the smart contract code are constrained by the vulnerability detection space, significantly varying Solidity versions, and no unified approach to verify against the ground truth. The primary contributions in this paper are (i) a standardized pre-processing method for smart contract training data, (ii) introducing bugs to create a balanced dataset of flawed files across Solidity versions using AST, and (iii) standardizing vulnerability identification using the Smart Contract Weakness Classification (SWC) registry. The ML models employed for benchmarking the proposed MLP, and a multi-input model combining MLP and Long short-term memory (LSTM) in our study are Random forest (RF), XGBoost (XGB), Support vector machine (SVM). The performance evaluation onreal-timesmart contracts deployed on the Ethereum Blockchain show an accuracy of up to 91% using MLP with the lowest average False Positive Rate (FPR) among all tools and models, measuring at 0.0125.
Abstract Blockchain smart contracts are codes that can execute and enforce rules for blockchain digital transactions. However, smart contracts may contain numerous subtle vulnerabilities, among which Ponzi vulnerabilities are notable. Existing Ponzi scheme contract detection approaches often rely on machine learning models trained on manually extracted features to achieve satisfactory classification results. Nonetheless, the code of a smart contract potentially harbours elusive semantics and characteristics, which compromises the precision and accuracy of vulnerability detection. Therefore, this paper proposes a method of converting operation codes into sequences to process data to avoid losing unnecessary important information, and uses a one‐dimensional convolutional neural network combined with formal verification. This method is named PZ‐C1DZ3(Ponzi‐Conv1D‐Z3) and is used for Ponzi scheme detection. Four types of machine learning models, namely Conv1D, Conv1D‐LSTM, Conv1D‐MLP, and Conv1D‐transformer, are employed for improvement and comparative validation experiments. Additionally, formal verification tool Z3 solver is utilized to conduct formal security verification on the final model, ensuring its safety. Experimental results demonstrate that the improved Conv1D model outperforms other existing models in terms of detection efficiency and accuracy while also meeting the requirements of formal security verification.
Abstract Money laundering has urged the need for machine learning algorithms for combating illicit services in the blockchain of cryptocurrencies due to its increasing complexity. Recent studies have revealed promising results using supervised learning methods in classifying illicit Bitcoin transactions of Elliptic data, one of the largest labelled data of Bitcoin transaction graphs. Nonetheless, all learning algorithms have failed to capture the dark market shutdown event that occurred in this data using its original features. This paper proposes a novel method named recurrent graph neural network model that extracts the temporal and graph topology of Bitcoin data to perform node classification as licit/illicit transactions. The proposed model performs sequential predictions that rely on recent labelled transactions designated by antecedent neighbouring features. Our main finding is that the proposed model against various models on Elliptic data has achieved state-of-the-art with accuracy and $$f_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>f</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -score of 98.99% and 91.75%, respectively. Moreover, we visualise a snapshot of a Bitcoin transaction graph of Elliptic data to perform a case study using a backward reasoning process. The latter highlights the effectiveness of the proposed model from the explainability perspective. Sequential prediction leverages the dynamicity of the graph network in Elliptic data.
The emergence of blockchain has ushered in a significant transformation in information systems research. Blockchain’s key pillars such as decentralization, immutability, and transparency have paved the path for extensive exploration in various research domains. This particular study is focused on electronic voting, aiming to improve voting procedures by making better use of the benefits offered by blockchain technology. Through a comprehensive review of existing literature, we highlight the potential benefits of blockchain-based electronic voting systems such as transparency, security, and efficiency. However, several challenges, such as scalability, personal data confidentiality, and ensuring robust identity verification, persist. Addressing these issues is necessary to unlock the full potential of blockchain-based electronic voting systems, thereby fostering the development of trustworthy election systems in the future.