Marlena Broniszewska, Wiktor B. Daszczuk, Denny B. Czejdo
Global digitalization has accelerated, leading to continuous online shopping and services growth. However, the centralized nature of traditional e-commerce platforms raises concerns about data control, privacy, and potential single points of failure. Blockchain technology offers a decentralized alternative that addresses these issues, improving online transactions with enhanced privacy and anonymity for users. This article explores the problem of anonymization in web services by developing an anonymous online auction system using blockchain and zero-knowledge proof (ZKP). We propose a solution that employs ZKP in two stages: first, to verify that a user is eligible to participate in the auction, thereby creating a list of potential bidders; second, to prove that a bid is placed by a user from this list without disclosing their identity. This approach allows participants to engage in auctions anonymously, protecting their interests from competitors and sellers. The system eliminates the need for a trusted third party by leveraging the blockchain immutable ledger for transparency, giving users full control over their data and participation. We present the architecture and implementation details of the system, demonstrating its feasibility and potential to enhance privacy and security in online auctions.
Muhammad Shoaib Farooq, Shahzada Fahad Munir, Muhammad Faraz Manzoor, Momina Shaheen
The increasing complexity of fraudulent activities requires advanced fraud detection systems, as existing solutions lack effectiveness due to two challenges. First, privacy concerns prevent financial institutions from sharing sensitive transaction data. Second, data imbalance causes biased models, as fraudulent transactions represent a small fraction of total transactions, leading to poor fraud detection performance. To address these challenges, we propose an AI‐driven adaptive federated learning (AFL) framework for credit card fraud detection (CCFD). AFL enables decentralized learning, allowing financial institutions to train a global fraud detection model collaboratively without sharing raw transaction data. The model aggregation is performance‐adaptive, weighting client contributions based on detection accuracy to ensure a robust global model. To overcome data imbalance, we introduce a multistep data balancing framework integrating Tomek links for undersampling, borderline‐SMOTE for oversampling, and cognitive sample pruning to remove misleading samples. To evaluate the robustness and generalizability of the proposed framework, we conducted experiments on both the widely used 2013 Kaggle dataset and the Sparkov simulated dataset (2019‐2020). The Sparkov dataset, which contains interpretable demographic and merchant‐level features, allowed us to test the model’s adaptability to diverse data sources. The results demonstrate that the proposed AFL framework, combined with advanced data balancing, significantly outperforms traditional models, achieving 99% accuracy, 99.5% precision, 99.4% recall, and 99% F1‐score on the Kaggle dataset, and 97.4% accuracy, 99.5% precision, 97.5% recall, and 98.4% F1‐score on the Sparkov dataset. This research highlights AI’s transformative role in finance, particularly in enhancing fraud detection systems with improved accuracy, robustness, security, and scalability.
Fraud risks are on the rise with the increase in cryptocurrency transactions; the traditional detection methods become inadequate. This paper proposes a hybrid deep-learning framework for real-time fraud detection in Bitcoin transactions. Algorithms like Random Forest, support vector machine (SVM), Logistic Regression, and XGBoost are used to analyse transaction patterns and anomalies with a high level of accuracy. Different models have been tested in transaction data for Bitcoin, and the best-performing model was XGBoost with an accuracy of 96.94%. Advanced machine learning techniques enrich a system through secure data-driven insights and real-time anomaly detection, thus enhancing fraud risk detection. The obstacles faced are scalability, privacy issues, and inability to adjust models according to the evolving fraud technique. Future advancements might deal with federated learning, encryption methods, and cross-platform prevention to make the detection of fraud more secure. This paper indicates how well deep learning-based detection of fraud can scale and work efficiently to strengthen trust in digital financial systems.
Illicit transaction detection on blockchain networks presents a critical challenge due to the pseudonymous, decentralized, and high-volume nature of decentralized finance (DeFi) ecosystems. Traditional machine learning models struggle to effectively capture the temporal dynamics and irregular patterns of illicit behavior, while graph-based methods often incur high computational costs and rely on static relational structures. In this paper, we propose a novel dual-attention framework—GAM-CoT Transformer—for robust transaction-level anomaly detection. The proposed model integrates two key components: a Global Attention Module (GAM) that adaptively reweights feature channels and temporal steps to emphasize salient patterns, and a Contextual Transformer (CoT) block that efficiently models short-range dependencies using grouped convolutions instead of full self-attention. This design enables the model to simultaneously achieve computational efficiency, temporal expressiveness, and improved detection sensitivity. We evaluate our approach on a real-world blockchain transaction dataset and demonstrate its superiority over conventional classifiers including Random Forest, XGBoost, and LSTM-based models. The GAM-CoT Transformer achieves higher recall and F1 scores, particularly for the minority illicit class, while maintaining fast convergence and deployment scalability. Our method offers a practical and effective solution for enhancing the security of blockchain systems through intelligent transaction behavior modeling.
As decentralized finance (DeFi) expands, Ethereum’s role as the backbone for digital asset exchange, smart contracts, and financial protocols has grown—but so has its exposure to fraud. Phishing, money laundering, and malicious contracts exploit its openness. Existing ML and deep learning models often lack the balance between speed, accuracy, and explainability needed for real-time blockchain analysis. High-performing models like transformers are accurate but too resource-heavy and opaque. This study leverages TabNet—a sparse-attention deep learning model optimized for tabular Ethereum transaction data. It dynamically selects relevant features during training, enhancing both interpretability and efficiency. With an accuracy of 0.86, precision of 0.80, and F1-score of 0.79, TabNet outperforms traditional models in fraud detection while remaining lightweight and transparent. Its feature-level insights make it ideal for environments where trust, latency, and transparency are crucial. The results position TabNet as a scalable, practical alternative for fraud detection in blockchain ecosystems.
Bitcoin is a decentralized cryptocurrency, which is rapidly growing and offering many advantages. Although its structure protects users from some types of fraud, it is not completely immune, while fraud detection in Bitcoin remains still relatively unexplored. In this paper, we use a graph to model Bitcoin transactions and benefit from the graph’s structure to overcome the lack of informative transaction and user data. We utilize network analysis for feature extraction and model fraud detection as a classification problem using a Deep Neural Network as our classifier. Furthermore, we propose a novel approach that combines a Variational Graph Autoencoder (VGAE), for deriving appropriate node and graph embeddings, and supervised learning to detect fraudulent Bitcoin transactions. Our experimental results show that the proposed approach, while also affected by high class imbalance, similarly to using only the graph-based features for classification, performs significantly better in detecting high-risk areas in the graph.
Edward Danso Ansong, Simon Bonsu Osei, Raphael Adjetey Adjei
The surge in identity fraud, driven by the rapid adoption of mobile money, internet banking, and e-services during the COVID-19 pandemic, underscores the need for robust cybersecurity solutions. Zero-Knowledge Proofs (ZKPs) e... | Find, read and cite all the research you need on Tech Science Press
Blockchain's decentralized characteristics have posed unique challenges and unlocked novel opportunities for the accounting and auditing sector. While the potential impact of blockchain and smart contracts on auditing has been raised, comprehensive studies remain scarce. Using the Solidity language, this study explores the viability of encoding into smart contracts specific auditing rules that can automatically identify suspicious transactions in common fraud schemes. To illustrate the feasibility, it presents a proof-of-concept framework encompassing system architecture, smart contract development, and workflow procedures. Simulation results demonstrate that blockchain-based smart contract approach in this study can effectively identify problematic transactions in near real-time. Consequently, this could help auditors to allocate audit resources to focus efforts on higher risk transactions. The findings provide implications for future studies on the application of smart contracts in auditing.
Amir M. Ebrahimi, Bram Adams, Gustavo A. Oliva, Ahmed E. Hassan
Software applications that run on a blockchain platform are known as DApps. DApps are built using smart contracts, which are immutable after deployment. Just like any real-world software system, DApps need to receive new features and bug fixes over time in order to remain useful and secure. However, Ethereum lacks native solutions for post-deployment smart contract maintenance, requiring developers to devise their own methods. A popular method is known as the upgradeability proxy contract (UPC), which involves implementing the proxy design pattern (as defined by the Gang of Four). In this method, client calls first hit a proxy contract, which then delegates calls to a certain implementation contract. Most importantly, the proxy contract can be reconfigured during runtime to delegate calls to another implementation contract, effectively enabling application upgrades. For researchers, the accurate detection of UPCs is a strong requirement in the understanding of how exactly real-world DApps are maintained over time. For practitioners, the accurate detection of UPCs is crucial for providing application behavior transparency and enabling auditing. In this paper, we introduce UPC Sentinel, a novel three-layer algorithm that utilizes both static and dynamic analysis of smart contract bytecode to accurately detect active UPCs. We evaluated UPC Sentinel using two distinct ground truth datasets. In the first dataset, our method demonstrated a near-perfect accuracy of 99%. The evaluation on the second dataset further established our method's efficacy, showing a perfect precision rate of 100% and a near-perfect recall of 99.3%, outperforming the state of the art. Finally, we discuss the potential value of UPC Sentinel in advancing future research efforts.
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.
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
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.
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.