The development of blockchain technology has promoted the growth of cryptocurrencies, but it has also provided criminals with opportunities to engage in fraudulent activities. Currently, Ethereum fraud detection models based on machine learning face challenges such as feature redundancy and class imbalance, which lead to a high false negative rate. To address these issues, this paper proposes an improved CT-ARF detector to enhance the detection of fraudulent activities on Ethereum. This approach combines the ARFBoost feature enhancement mechanism and the CTDA (Conditioned Table Generative Adversarial Network-based) data augmentation strategy. First, we apply Recursive Feature Elimination (RFE) to select the most relevant and representative features from Ethereum transaction data, removing redundant or irrelevant features to improve data quality and obtain an optimal feature subset. Next, we design a multi-dimensional synchronized embedding technique that integrates performance evaluation metrics for each feature into the embedding model, thereby enhancing the contribution of each feature to target identification. Additionally, we use CTDA for data augmentation to address the class imbalance issue, further improving the model’s robustness and accuracy. Finally, we evaluate a CT-ARF detector with the Ethereum fraud detection dataset using LightGBM, XGBoost, CatBoost, AdaBoost, RF, DT, and KNN. The results demonstrate that a CT-ARF detector effectively improves the recall rate of Ethereum fraud detection while reducing the false negative rate.
Ze Chang, Yunfei Cai, Xiao Fan Liu, Zhenping Xie · 6 authors
With the rapid development of blockchain technology, fraudulent activities have significantly increased, posing a major threat to the personal assets of blockchain users. The blockchain transaction network formed during user transactions can be represented as a graph consisting of nodes and edges, making it suitable for a graph data structure. Fraudulent nodes in the transaction network are referred to as anomalous nodes. In recent years, the mainstream method for detecting anomalous nodes in graphs has been the use of graph data mining techniques. However, anomalous nodes typically constitute only a small portion of the transaction network, known as the minority class, while the majority of nodes are normal nodes, referred to as the majority class. This discrepancy in sample sizes results in class imbalance data, where models tend to overfit the features of the majority class and neglect those of the minority class. This issue presents significant challenges for traditional graph data mining techniques. In this paper, we propose a novel graph neural network method to overcome class imbalance issues by improving the Graph Attention Network (GAT) and incorporating ensemble learning concepts. Our method combines GAT with a subtree attention mechanism and two ensemble learning methods: Bootstrap Aggregating (Bagging) and Categorical Boosting (CAT), called SGAT-BC. We conducted experiments on four real-world blockchain transaction datasets, and the results demonstrate that SGAT-BC outperforms existing baseline models.
With the rapid growth of blockchain technology, smart contracts are now crucial to Decentralized Finance (DeFi) applications. Effective vulnerability detection is vital for securing these contracts against hackers and enhancing the accuracy and efficiency of security audits. In this paper, we present SimilarGPT, a unique vulnerability identification tool for smart contract, which combines Generative Pretrained Transformer (GPT) models with Code-based similarity checking methods. The main concept of the SimilarGPT tool is to measure the similarity between the code under inspection and the secure code from third-party libraries. To identify potential vulnerabilities, we connect the semantic understanding capability of large language models (LLMs) with Code-based similarity checking techniques. We propose optimizing the detection sequence using topological ordering to enhance logical coherence and reduce false positives during detection. Through analysis of code reuse patterns in smart contracts, we compile and process extensive third-party library code to establish a comprehensive reference codebase. Then, we utilize LLM to conduct an indepth analysis of similar codes to identify and explain potential vulnerabilities in the codes. The experimental findings indicate that SimilarGPT excels in detecting vulnerabilities in smart contracts, particularly in missed detections and minimizing false positives.
With the rapid development of blockchain technology and the popularity of cryptocurrency, phishing scams pose an increasingly severe threat to the security of cryptocurrency transactions. Existing fraud detection methods have not accurately identified phishing behaviors, especially failing to capture key neighbor information and its impact effectively. To address this problem, we proposed a phishing detection framework based on FAAN-GBM (Feature and Attention Augmented Network with Gradient Boosting Machine), which aims to improve phishing fraud detection effectiveness on the Ethereum platform by further refining the extraction of phishing account features. This framework integrates basic features, transaction features, and interaction features of nodes, optimizes feature aggregation through importance analysis and attention mechanism of neighbor node, and uses autoencoders to deepen the nonlinear expression of node features. Through extensive testing on real Ethereum datasets, FAAN-GBM has demonstrated superior performance over existing methods, effectively improving the identification accuracy of phishing fraud nodes.
Ponzi schemes deceive investors with promises of high returns, relying on funds from new investors to pay earlier ones, creating a misleading appearance of profitability. These schemes are inherently unsustainable, collapsing when new investments wane, leading to significant financial losses. Many researchers have focused on detecting such schemes, but challenges remain due to their evolving nature. This study proposes a novel hybrid machine-learning approach to enhance Ponzi scheme detection. Initially, we train an XGBoost classifier and extract its features. Meanwhile, we tokenize opcode sequences, train a gated recurrent unit (GRU) model on these sequences, and extract features from the GRU. By concatenating the features from the XGBoost classifier and the GRU, we train a final XGBoost model on this combined feature set. Our methodology, leveraging advanced feature engineering and hybrid modeling, achieves a detection accuracy of 96.57%. This approach demonstrates the efficacy of combining XGBoost and GRU models, along with sophisticated feature engineering, in identifying fraudulent activities in Ethereum smart contracts. The results highlight the potential of this hybrid model to offer more robust and accurate Ponzi scheme detection, addressing the limitations of previous methods.
Decentralisation is the next booming thing. One of the major applications of decentralisation is cryptocurrencies which are deployed on a blockchain architecture. Almost ev-eryone in the world has been introduced to cryptocurrency due to its massive outreach. It is a form of currency but in digital form and way more valuable. Cryptocurrency has amassed a lot of young followers due to its high lucrative returns. With Bitcoin reaching new peaks, it has directly put cryptocurrencies in cross hairs of hustlers who want to fraud their way into wealth. The rate of fraud cases in crypto markets has been increasing linearly. To avoid such cases this paper will propose a novel deep neural network architecture called CryptoFraudNet which will make use Graphs components, Self Attention mechanism, etc. to capture even the smallest discrepancies in data.
With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anony... | Find, read and cite all the research you need on Tech Science Press
Ethereum is an application platform that distributes versions of intelligent contracts to thousands of people globally, utilizing blockchain to decentralize data. Ethereum is a global currency that is used to exchange value without requiring supervision or outside involvement. However, as e-commerce grows, the biggest threat to trade security is the proliferation of illegal activities like phishing, money laundering, and bribery. The need for strong defenses is highlighted by the inherent hazards of blockchain technology, such as the potential for fraud and cyberattacks. The stability and preservation of confidence in the financial system depend on an accessible network that is impervious to these kinds of assaults. Different classification algorithms, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) are being used in this work to detect Ethereum fraud. We are utilizing a dataset that includes rows of Ethereum cryptocurrency transactions along with rows of known unauthorized transactions. Notably, the “XGBoost” model identifies differences that might draw attention and avert possible problems in this task. Supervised learning for Ethereum fraud detection improves the ecosystem’s security and integrity.
Academic honesty is the cornerstone of educational excellence. But challenges related to fraud and record tampering remain. This article explores the application of blockchain technology as a transformative solution to enhance academic integrity in educational institutions. Blockchain's decentralized and immutable ledger provides a secure framework for managing academic credentials. This reduces fraud and ensures transparency. The study begins with an overview of current challenges in managing academic records. It highlights vulnerabilities to counterfeiting and inefficiencies in the verification process. We then propose a blockchain-based system to automate and secure certificate issuance and authentication through smart contracts to existing academic records management systems. A Proof-of-Stake consensus mechanism. It is used to balance network security and integration efficiency. This ensures that stakeholders with significant investments in the system are encouraged to act honestly. Empirical results show that blockchain systems improve data security. Increase transparency and increase efficiency of record management Performance indicators such as transaction throughput Inspection time and the efficiency of the consensus mechanism It emphasizes the system's ability to handle large volumes of data while maintaining operational integrity. This research concludes that blockchain technology offers a robust solution to contemporary challenges in academic integrity. By providing a transparent method effective and more secure academic record management. Additionally, this article suggests avenues for future research. Including scalability and integration with lifelong learning certification.
With the development of deep learning, especially driven by advanced models such as Graph Neural Networks (GNN), smart contract vulnerability detection is gradually moving toward automation and intelligence. Although existing deep learning detection methods have improved the efficiency of vulnerability detection to some extent, they fail to fully explore and utilize the rich syntactic and semantic information in smart contracts and generally suffer from insufficient feature extraction. In this paper, we propose a new method for smart contract vulnerability detection that combines a Multi-Information Contract Graph (MIG) with a Hierarchical Graph Feature Extraction model (HGFE). MIG integrates key information such as control flow, data flow, and vulnerability feature flow within smart contracts, fully mining and utilizing the rich syntactic and semantic features of smart contracts, providing the model with comprehensive feature representation. HGFE applies a multilayer feature extraction strategy, combining global and local feature extraction, and comprehensively considers multiple dimensions of information within the contract graph, thereby fully extracting the features of the contract graph. The experimental results demonstrate that our method significantly enhances the ability to detect potential vulnerabilities in smart contracts, achieving a maximum accuracy and precision of 97.29% and 97.70%, respectively, outperforming other advanced methods.
Roozbeh Sarenche, Ren Zhang, Svetla Nikova⋆, Bart Preneel
A Bitcoin miner who owns a sufficient amount of mining power can perform selfish mining to increase its relative revenue. Studies have demonstrated that the time-averaged profit of a selfish miner starts to rise once the mining difficulty level gets adjusted in favor of the attacker. Selfish mining profitability lies in the fact that orphan blocks are not incorporated into the current version of Bitcoin’s difficulty adjustment mechanism (DAM). Therefore, it is believed that considering the count of orphan blocks in the DAM can result in complete unprofitability for selfish mining. In this paper, we disprove this belief by providing a formal analysis of the selfish mining time-averaged profit. We present a precise definition of the orphan blocks that can be incorporated into calculating the next epoch’s target and then introduce two modified versions of DAM in which both main-chain blocks and orphan blocks are incorporated. We propose two versions of smart intermittent selfish mining, where the first one dominates the normal intermittent selfish mining, and the second one results in selfish mining profitability under the modified DAMs. Moreover, we present the orphan exclusion attack with the help of which the attacker can stop honest miners from reporting the orphan blocks. Using combinatorial tools, we analyze the profitability of selfish mining accompanied by the orphan exclusion attack under the modified DAMs. Our results show that even when considering orphan blocks in the DAM, selfish mining can still be profitable. However, the level of profitability under the modified DAMs is significantly lower than that observed under the current version of Bitcoin DAM, suggesting that orphan reporting can be an effective countermeasure against a payoff-maximizing selfish miner.
The integration of Blockchain and Machine Learning (ML) technologies offers a transformative approach to combating fraud across various sectors, including finance, healthcare, and cybersecurity. Blockchain's decentralized and immutable nature ensures data integrity and transparency, while Machine Learning algorithms enable the detection of intricate fraud patterns through predictive analytics and anomaly detection. This synergistic combination provides a robust mechanism for identifying fraudulent activities in real time, minimizing human error, and optimizing decision-making processes. In the financial sector, Blockchain enhances the security and transparency of transactions, while ML models analyze transaction data to identify unusual patterns that may indicate fraud. In healthcare, Blockchain ensures the secure sharing of medical records, and ML assists in detecting fraudulent claims and potential identity theft. Cybersecurity applications leverage Blockchain for secure communication and data storage, with ML identifying potential threats or vulnerabilities. By combining these two cutting-edge technologies, organizations can strengthen their fraud detection systems, improve trust, and mitigate the risks associated with financial losses, data breaches, and privacy violations. This paper explores the multidisciplinary synergy of Blockchain and ML, illustrating their potential to revolutionize fraud detection mechanisms across multiple domains, providing a comprehensive overview of current advancements, challenges, and future directions for their integration in the fight against fraud.
Shabnam Fazliani, Mohammad Mowlavi Sorond, Arsalan Masoudifard
The advent of smart contracts has enabled the rapid rise of Decentralized Finance (DeFi) on the Ethereum blockchain, offering substantial rewards in financial innovation and inclusivity. This growth, however, is accompanied by significant security risks such as illicit accounts engaged in fraud. Effective detection is further limited by the scarcity of labeled data and the evolving tactics of malicious accounts. To address these challenges with a robust solution for safeguarding the DeFi ecosystem, we propose $\textbf{SLEID}$, a $\textbf{S}$elf-$\textbf{L}$earning $\textbf{E}$nsemble-based $\textbf{I}$llicit account $\textbf{D}$etection framework. SLEID uses an Isolation Forest model for initial outlier detection and a self-training mechanism to iteratively generate pseudo-labels for unlabeled accounts, enhancing detection accuracy. Experiments on 6,903,860 Ethereum transactions with extensive DeFi interaction coverage demonstrate that SLEID significantly outperforms supervised and semi-supervised baselines with $\textbf{+2.56}$ percentage-point precision, comparable recall, and $\textbf{+0.90}$ percentage-point F1 -- particularly for the minority illicit class -- alongside $\textbf{+3.74}$ percentage-points higher accuracy and improvements in PR-AUC, while substantially reducing reliance on labeled data.
This study aims to examine the function of blockchain technology to detect fraud in health insurance. we consider the literature on fraud in health insurance, blockchain, and smart contracts to to test a newly structured software system based on blockchain technology for this purpose. Different blockchain platforms, consensus algorithms, and structures have been used to pick the proposed system’s best structure based on blockchain. Eventually, the best techniques to put the system to the test and evaluate the findings were assessed. we propose a standardized system, where blockchain is applied to store data and smart contracts are used to automate insurance policies. Furthermore, a web-based application, which acts as core insurance software, is proposed for all stakeholders to communicate with the blockchain and smart contracts. Therefore, the proposed system comprises a blockchain, web app, and standardized smart contracts. The proposed system mainly focuses on fraud detection in insurance claims while maintaining a standard data storage and transfer structure. The system proved to be thriving once claim data can be created, read, and analyzed (i.e. fraudulent data are caught) effectively in a standard way. The web app consists of a front-end and back-end section. The front-end enables users to interact with the proposed system, and the back-end allows the insurance company to store records on the blockchain and increase the chances of detecting fraud in insurance claims, especially Digital Insurance Claims. Finally, a blockchain-based web application that can be used as core insurance software for any health insurance company is proposed.
This paper presents a comprehensive analysis of adaptive behavioral authentication systems designed for fraud detection in financial services. These systems leverage real-time user behavior, such as typing patterns, mouse movements, and geolocation data, to continuously monitor and assess authentication risks. A layered approach integrates behavioral analysis with traditional credentials, providing enhanced security against evolving fraud techniques. The proposed system illustrates the interaction between users, the authentication system, a behavioral engine, and fraud detection models, enabling dynamic decision-making processes. The proposed framework enhances fraud detection by ensuring robust monitoring without compromising user experience. Future work aims to address challenges in data privacy, ethical considerations, and system adaptability to emerging financial technologies like decentralized finance (DeFi).
As the demand for cryptographic assets increases, so does the number of fraudulent transactions, necessitating efficient detection methods. In this paper, we propose a method to determine whether a set of transaction data contains fraudulent transactions. We apply topological data analysis, which characterizes the geometric structure of the data, to Ethereum, one of the crypto assets. Our aim is to solve the imbalance in the transaction data used in machine learning models for fraudulent transaction detection. Our method achieved an F1 score of 0.9891 on a set of transaction data containing 10 fraudulent transactions out of 10000 transactions.
Smart contracts are essential for managing digital assets in blockchain networks, highlighting the need for effective security measures. This paper introduces SmartLLMSentry, a novel framework that leverages large language models (LLMs), specifically ChatGPT with in-context training, to advance smart contract vulnerability detection. Traditional rule-based frameworks have limitations in integrating new detection rules efficiently. In contrast, SmartLLMSentry utilizes LLMs to streamline this process. We created a specialized dataset of five randomly selected vulnerabilities for model training and evaluation. Our results show an exact match accuracy of 91.1% with sufficient data, although GPT-4 demonstrated reduced performance compared to GPT-3 in rule generation. This study illustrates that SmartLLMSentry significantly enhances the speed and accuracy of vulnerability detection through LLM-driven rule integration, offering a new approach to improving Blockchain security and addressing previously underexplored vulnerabilities in smart contracts.
Gibran Gómez, Kevin van Liebergen, Davide Sanvito, Giuseppe Siracusano · 6 authors
Cryptocurrency abuse reporting services are a valuable data source about abusive blockchain addresses, prevalent types of cryptocurrency abuse, and their financial impact on victims. However, they may suffer data pollution due to their crowd-sourced nature. This work analyzes the extent and impact of data pollution in cryptocurrency abuse reporting services and proposes a novel LLM-based defense to address the pollution. We collect 289K abuse reports submitted over 6 years to two popular services and use them to answer three research questions. RQ1 analyzes the extent and impact of pollution. We show that spam reports will eventually flood unchecked abuse reporting services, with BitcoinAbuse receiving 75% of spam before stopping operations. We build a public dataset of 19,443 abuse reports labeled with 19 popular abuse types and use it to reveal the inaccuracy of user-reported abuse types. We identified 91 (0.1%) benign addresses reported, responsible for 60% of all the received funds. RQ2 examines whether we can automate identifying valid reports and their classification into abuse types. We propose an unsupervised LLM-based classifier that achieves an F1 score of 0.95 when classifying reports, an F1 of 0.89 when classifying out-of-distribution data, and an F1 of 0.99 when identifying spam reports. Our unsupervised LLM-based classifier clearly outperforms two baselines: a supervised classifier and a naive usage of the LLM. Finally, RQ3 demonstrates the usefulness of our LLM-based classifier for quantifying the financial impact of different cryptocurrency abuse types. We show that victim-reported losses heavily underestimate cybercriminal revenue by estimating a 29 times higher revenue from deposit transactions. We identified that investment scams have the highest financial impact and that extortions have lower conversion rates but compensate for them with massive email campaigns.