Blockchain smart contracts are pivotal to decentralized applications, yet their security remains a critical challenge. This study analyzes a dataset of 1,000 smart contracts to investigate known vulnerabilities, audit practices, and exploit patterns. The results reveal that audited contracts are significantly less prone to exploitation, with 75% exhibiting no exploit history compared to 55% of non-audited contracts. "Integer Overflow" and "Unchecked Call" were identified as the most prevalent vulnerabilities, contributing to 60% and 50% exploit rates, respectively. The study highlights the importance of transparent audit reporting, as contracts without available reports were exploited in 35% of cases. Additionally, hidden vulnerabilities in ostensibly secure contracts underscore the evolving sophistication of blockchain threats. This research emphasizes the need for robust security practices, including stricter coding standards, comprehensive audits, and advanced vulnerability detection techniques such as formal verification and machine learning. Future works aim to integrate security tools into development workflows and foster industry-wide collaboration to standardize auditing practices, thereby enhancing the security and trustworthiness of blockchain ecosystems.
Humans have the ability to incrementally learn, accumulate, update, and apply knowledge from dynamic environments. This capability, known as continual learning or lifelong learning, is also a long-term goal in the development of artificial intelligence. However, neural network-based continual learning suffers from catastrophic forgetting: the acquisition of new knowledge typically disrupts previously learned knowledge, leading to partial forgetting and a decline in the model’s overall performance. Most current continual learning methods can only mitigate catastrophic forgetting and fail to incrementally improve the overall performance. In this work, we aim to incrementally improve performance within sample incremental context by utilizing inter-stage edges as a pathway for explicit knowledge transfer in continual graph learning. Building on this pathway, we propose a knowledge-augmented replay method by leveraging evolving subgraphs of important nodes. This method enhances the distinction between patterns associated with different node classes and consolidates previously learned knowledge. Experiments on phishing detection in Ethereum transaction networks validate the effectiveness of the proposed method, demonstrating effective knowledge retention and augmentation while overcoming catastrophic forgetting and incrementally improving performance. The results also reveal the relationship between average accuracy and average forgetting. Lastly, we identify the key factor to incremental performance improvement, which lays a foundation for convergence of continual graph learning.
Karan Maheshwari, Srujan Kumar Ch., Y. V. Srinivasa Murthy, Anand Paul
Phishing attacks in Ethereum transactions pose a significant threat to the security and integrity of blockchain-based systems, as these scams exploit user vulnerabilities to extract sensitive information or cryptocurrency assets. In contrast to the approaches proposed by the various research works to tackle phishing detection, many struggle with high-dimensional datasets, leading to computational inefficiencies and overfitting. To address these gaps, this study applies principal component analysis (PCA) for dimensionality reduction, helping in the development of more efficient and robust machine learning models by reducing data complexity and enhancing model generalization. A comparative analysis is conducted using multiple algorithms, including support vector machines (SVMs), decision trees (DT), XGBoost, and multi-layer perceptron (MLP). By evaluating their performance using standard metrics such as accuracy, F1 score, precision, recall, and ROC-AUC, the MLP model demonstrates superior accuracy and generalization, establishing its efficacy for phishing detection in Ethereum transactions. This work highlights the importance of feature reduction techniques and neural network models in enhancing the accuracy and efficiency of phishing detection systems, paving the way for future advancements in blockchain security.
Ponzi schemes, a more than a century-old fraud, have recently infiltrated blockchain-based cryptocurrency domain led by an explosion of such schemes in two most popular cryptocurrencies: Bitcoin and Ethereum. On these two platforms alone, the perpetrators of these frauds have fleeced gullible investors of billions of dollars annually. Smart Ponzi schemes are a hazard to these cryptocurrency ecosystems, diminishing investor confidence in these cutting-edge technologies, threatening their integrity, and hindering their growth and broader adaptation. These smart Ponzi schemes have also created a nightmare for law enforcement as tracking and taking countermeasures against fraudsters and recovering the victims’ investment is challenging. Over the years, researchers have utilized significant advances in machine learning and AI to detect and promptly caution users against investing in Ponzi schemes on Bitcoin and Ethereum. However, this research still exists in silos, and there is a lack of a detailed survey paper critically analyzing various aspects of the approaches focusing on the menace of smart Ponzi schemes. This article surveys the state-of-the-art techniques proposed in the literature to detect smart Ponzi schemes on two popular blockchain platforms: Bitcoin and Ethereum. We list, categorize, and discuss papers that contributed benchmark datasets, developed novel features concerning various aspects of smart Ponzi schemes, and proposed novel machine-learning approaches to detect them.
Jianyu Qu, Li Ruan, Limin Xiao, Liya Hu · 5 authors
As blockchain technology advances at an unprecedented pace, phishing scams increasingly exploit vulnerabilities in Ethereum transactions. These attacks typically involve fraudulent addresses that deceive users and illicitly expropriate digital assets, posing significant threats to the security and integrity of the blockchain ecosystem. In this work, we propose txnet2vec, a novel framework for detecting phishing addresses based on transaction graph analysis. Our approach begins by collecting labeled Ethereum transaction data and constructing a directed, weighted transaction graph, where nodes represent addresses and edges denote transactions. To capture both structural and transactional characteristics, we employ network embedding techniques to learn low-dimensional representations of addresses. To further improve detection accuracy, we design an attention-based feature fusion mechanism that integrates multiple random-walk-based sampling strategies, incorporating transaction amounts, temporal features, and market-driven behaviors. The learned embeddings are then fed into a Support Vector Machine classifier to distinguish phishing from benign addresses. Extensive experimental results demonstrate that txnet2vec achieves superior performance compared to existing baselines in phishing detection within Ethereum’s transaction network.
Federated Learning (FL) enables collaborative model training across maritime devices without the need to share raw data. However, challenges such as data heterogeneity and unreliable marine communications impede its performance and security. In this work, we propose a Blockchain-based Active Client Selection Strategy for FL in Shipping (BAFLS), which utilizes blockchain technology to create a secure and auditable environment for node registration and parameter exchange. A lightweight consensus algorithm is introduced to dynamically elect aggregation nodes based on residual energy, reputation, and computing power, improving fault tolerance and reducing resource consumption. Based on such, a Top-kactive learning strategy is designed to select the most informative clients, balancing data utility and privacy protection. Security evaluation and analysis demonstrate that BAFLS effectively resists aggregation attacks and privacy inference. Experimentations on FMNIST, HAR, and ShipNetwork10 datasets show that BAFLS achieves up to 2.4% higher accuracy, reduces convergence rounds by up to 44%, and consistently lowers communication overhead compared to the baseline under various degrees of label and feature heterogeneity.
The recurring issue of smart contract security breaches has heightened concerns about their reliability and safety, making contract security a critical challenge in the blockchain space. Existing traditional detection techniques predominantly utilize static, expert-defined rule sets, which inherently introduce limitations including reliance on expert knowledge, compromised detection accuracy, and a constrained scope of identifiable vulnerabilities. Therefore, this paper proposes Vul-Sensitive opcode weighting for multi-label smart contract vulnerability detection. The proposed method first processes the source code of smart contracts by converting it to bytecode and then extracts opcodes. Based on a predefined set of critical instructions, Vul-Sensitive opcodes are weighted to enhance the representation of vulnerability-related features. Then, the final feature matrix is utilized by deep learning models for training and classification. To assess the effectiveness of the proposed approach, this paper compares various deep neural network architectures before and after optimization. Experimental results show that it significantly enhances vulnerability detection across all models and consistently outperforms non-weighted methods by effectively strengthening feature representation, achieving the best Micro-F1 score of 90.65%, which validates its effectiveness in multi-vulnerability detection tasks.
With the rapid development of the Ethereum platform, phishing fraud has become increasingly rampant, posing significant security risks to both users and the platform. However, existing phishing fraud detection methods are manually designed, requiring substantial human effort, and are unable to adapt to diverse detection scenarios. In this article, we propose phishing detection on Ethereum via graph neural architecture search of transaction subgraph (PETS-GNAS). The phishing detection problem on Ethereum is transformed into a graph classification task, where accounts and transactions are represented as nodes and edges, respectively. Specifically, we acquire account labels and their corresponding transaction information from credible sources and then extract transaction subgraphs centered on labeled accounts as datasets. Subsequently, we introduce a mapping mechanism to extend these transaction subgraphs into corresponding temporal transaction subgraph (TTSG), encoding transaction attributes during the TTSG construction process. Then, graph neural architecture search (GNAS) strategy that incorporates early stopping and L2 regularization is proposed to enhance the feasibility and accuracy of Ethereum phishing detection by avoiding redundant parameters and complex architectures. Extensive experimental results demonstrate that PETS-GNAS achieves strong performance in phishing detection tasks, enabling early and accurate identification of phishing accounts.
This paper addresses the problem of detecting money laundering in the Bitcoin network. Money laundering is the process of handling the proceeds of crime to conceal their illegal source, these illicit transactions have complex features, similar to those of legal transactions. It is well known that transactions can be represented as topological graph structure data, and many GCN-based methods have been developed for Anti-Money Laundering (AML) tasks. However, existing methods have not performed as well in dynamically assigning weights to neighboring nodes and extracting information from global nodes in the Bitcoin network. Therefore, we identify three major challenges: Firstly, GCNs can be misled by concealed illegal transactions due to uniform node representation weights. Secondly, current node-level GCNs cannot handle varied methods of concealing illegal transactions because they fail to extract global information. Thirdly, the costliness of data labelling necessitates the effective use of limited but rich domain-specific labelled data. To address these challenges, we propose the Transformer-enhanced Graph Attention Network (TFGAT) with a Global-Local Attention Mechanism (GLATM) that uses Transformers to extract global information and selectively focus on local information from connected nodes. Due to the limited availability of labelled data from expensive data labelling processes, we introduce a Deep Cyclic Pseudo-Label Updating Mechanism (DCPLU) to enhance data distribution and model robustness, which does not rely on manifold structure or Euclidean distance assumptions. DCPLU can enhance model performance while preserving the model's existing parameters, enabling it to maintain its current faster response time in the application scenario. Experimental results show that our methods outperform existing models across various metrics.
With the advent of Web3, many online services have been revolutionised through decentralisation. With blockchain as the main decentralisation engine, online social media platforms have witnessed a new life. Recently, memes have come into play, providing new opportunities for community aggregation, giving birth to the so-called memecoin phenomenon. Memecoins are blockchain-backed cryptocurrencies that can be freely traded, and people can discuss them online. They represent a new scenario in which social and economic aspects are tightly intertwined, but external figures or events can also influence the activity of each token. In this paper, we provide an analysis of the relationship between the social and economic spheres of memecoins and how external factors influence their activity. We conducted our analyses on six case studies taken from different contexts and with unique histories, downloading data from various subreddits and blockchains. The findings show that each memecoin has unique distinctive features and that the activity around them is influenced by crypto-influencers, scams, or even external events.
Background: Cryptocurrency is a digital decentralized currency that enables peer-to-peer transactions without the involvement of intermediaries, using blockchain technology to ensure security and transparency. These emotions can help understand when and what behavioral intentions toward cryptocurrency adoption are necessary. These insights contribute to the growing literature on fintech adoption in emerging economies and provide valuable guidance for policymakers and investors formulating cryptocurrency regulations in Nepal. Objectives: It aims to investigate the influence of perceived usefulness, perceived ease of use, trust, and perceived risk constructs attached to an individual's intention to be involved in cryptocurrency transactions. It finally aims to study the relationships among these variables and their effect on cryptocurrency adoption in the Nepal context. Methods: The study uses a quantitative descriptive design and snowball sampling alone to extract information from cryptocurrency users in Nepal. A structured online questionnaire was used to gather 272 responses and analyzed using correlation and regression to investigate the significance of the proposed hypotheses. Results: Findings show that perceived usefulness, ease, or trust significantly and positively influence behavioral intention toward cryptocurrency adoption, while perceived risk harms adoption because users worry about market volatility, regulatory uncertainty, and security threats. Despite the government's legal restrictions, many Nepalese citizens are still involved in cryptocurrency transactions, seeing it as a source of financial benefit and investment opportunity. Conclusion: The study attests that perceived usefulness, perceived ease of use, and trust motivate behavior toward adopting cryptocurrency, while perceived risk provides a barrier. This enlightens policymakers on balanced regulatory measures that address risks while encouraging innovation in digital finance. These findings would provide valuable implications for policymakers, financial institutions, and technology developers in outlining the future of cryptocurrency regulation and adoption strategies. JEL Classification: D14, E42, G41
Chunhong Liu, Zihang Sang, Li Duan, Jingxiong Wang · 6 authors
Security vulnerabilities in smart contracts can have severe economic consequences. Existing smart contract vulnerability detection methods rely primarily on rigid rules defined by experts and have difficulty in detecting unknown vulnerabilities. This article proposes a new Anomalous Smart Contract Detector, named ASCD, to effectively detect known and unknown vulnerabilities in smart contracts. This is achieved by interpreting unknown vulnerabilities as code anomalies and detecting them with an anomaly detection technique named DeepSVDD. This is also attributed to a new design of feature extraction, in which we compile smart contract source codes into opcodes, extract semantic features from opcode sequences, and control flow features from control flow graphs. By joining LSTM and GIN, the semantic and control flow features are fused to offer a comprehensive representation of smart contracts suitable for anomaly detection. Extensive experiments were conducted to verify the ASCD model, and more than 30,000 smart contracts were tested. The new model offers a significantly better F1-score than existing methods in detecting known vulnerabilities and achieves a high accuracy of 77% in detecting unknown vulnerabilities.
Pasquale De Rosa, Simon Queyrut, Yérom-David Bromberg, Pascal Felber · 5 authors
The Ethereum Virtual Machine (EVM) is a decentralized computing engine. It enables the Ethereum blockchain to execute smart contracts and decentralized applications (dApps). The increasing adoption of Ethereum sparked the rise of phishing activities. Phishing attacks often target users through deceptive means, e.g., fake websites, wallet scams, or malicious smart contracts, aiming to steal sensitive information or funds. A timely detection of phishing activities in the EVM is therefore crucial to preserve the user trust and network integrity. Some state-of-the art approaches to phishing detection in smart contracts rely on the online analysis of transactions and their traces. However, replaying transactions often exposes sensitive user data and interactions, with several security concerns. In this work, we present PhishingHook, a framework that applies machine learning techniques to detect phishing activities in smart contracts by directly analyzing the contract’s bytecode and its constituent opcodes. We evaluate the efficacy of such techniques in identifying malicious patterns, suspicious function calls, or anomalous behaviors within the contract’s code itself before it is deployed or interacted with. We experimentally compare 16 techniques, belonging to four main categories (Histogram Similarity Classifiers, Vision Models, Language Models and Vulnerability Detection Models), using 7,000 real-world malware smart contracts. Our results demonstrate the efficiency of PhishingHook in performing phishing classification systems, with about 90% average accuracy among all the models. We support experimental reproducibility, and we release our code and datasets to the research community.
T. C. Jermin Jeaunita, Thiyagarajan Ramesh, C. V. Manjushree, P. T. Shantala
Crowdfunding has become essential for financing entrepreneurial projects, innovative projects, and social initiatives.However, existing platforms face critical challenges, including a lack of transparency, low user engagement, data privacy concerns, and ineffective personalization of user experiences.To address these limitations, this study introduces a novel decentralized crowdfunding framework that integrates Federated Learning (FL), blockchain technology, and Q-learning to enhance security, transparency, and user engagement.The framework leverages FL to collaboratively train models across distributed datasets while ensuring privacy preservation by eliminating the need to share raw user data.Blockchain technology is utilized to ensure tamper-proof transaction records and automate trustless interactions through smart contracts, effectively preventing fraud while increasing transparency.Additionally, a Q-learning-based incentive mechanism is incorporated to predict and stimulate user engagement, ensuring dynamic long-term engagement.The experimental evaluation illustrates that the designed framework attains state-of-the-art performance with an accuracy rate of 99.39%, surpassing existing methodologies.The results demonstrate the effectiveness of the framework in providing a secure, decentralized, and highly personalized crowdfunding system, raising trust and engagement among stakeholders and resolving long-standing issues in crowdfunding platforms.
Zsofia Baruwa, Sanjay Bhattacherjee, Sahil Rey Chandnani, Zhen Zhu
This work is the first study on the perceptions of social media users about cryptocurrency attacks. The double-spending or 51% attack being the most fundamental attack on cryptocurrencies, it is the focus of this study. As a first step, we create a first-of-its-kind comprehensive list of 31 events of 51% attacks on various proof-of-work cryptocurrencies, showing that these events are quite common. This list contradicts the general perception about the security of cryptocurrencies, particularly portrayed in the Executive Order establishing a Strategic Bitcoin Reserve and a Digital Asset Stockpile in the US. We design the methodologies for our new study of user perceptions around these attacks. We create datasets containing tweets from the time of the attack events, and compare them with benchmark data from normal times. We define parameters for profiling these datasets based on user perceptions – sentiments and emotions. We study the variation of these perception profiles, when a cryptocurrency is under attack and the benchmark otherwise, between multiple attack events of the same cryptocurrency, and between different cryptocurrencies. Our results confirm some expected overall behaviour and reactions while providing nuanced insights that may not be obvious or may even be considered surprising. Our code and datasets are publicly accessible.
G. Ramesh, Kiran Raj K M, Anagha Ankolekar, Gautam Kamath · 5 authors
Social media has significantly altered the way we interact and connect. However, with unprecedented connectivity and information availability, significant challenges arise concerning data privacy, ethics, and transparency. The paper explores the intersection of data ethics and privacy within social media, particularly unclear privacy policies, biased algorithms, and the spread of misinformation. This paper not only explores these challenges but also examines emerging solutions such as Self-Sovereign Identity (SSI) and regulatory measures like GDPR. It emphasizes the need for clear consent, openness, and accountability from platform providers to protect user data and ensure fairness. The paper also explores how emerging technologies like SelfSovereign Identity (SSI) and Web3 can give users more control over their data, helping to reduce misuse. In addition, it discusses ethical challenges related to algorithmic decision-making and misinformation, proposing ways to detect and prevent harm. Lastly, the paper calls for a rethink of “Tech Ethics>” that is more focused on social welfare and redressing inequalities. Using a thorough and analytical approach, this paper aims to create a culture of privacy that encourages participation and supports stronger regulations to address data ethics issues in today's fastevolving digital landscape.
This study explores the intricate relationship between public sentiment and Bitcoin market dynamics, leveraging sentiment analysis of Twitter data to uncover patterns in emotional discourse surrounding cryptocurrency. By analyzing sentiment trends from 2013 to 2019, the research reveals a cyclical interplay between positive and negative sentiment, often aligning with Bitcoin’s dramatic price movements. Positive sentiment peaks coincide with periods of market optimism, driven by narratives of technological innovation and mainstream adoption, while negative sentiment troughs reflect moments of fear, uncertainty, and doubt (FUD) during market corrections. Despite the observed alignment, the correlation between sentiment and Bitcoin prices remains weak, underscoring the complexity of market behavior and the influence of external factors such as macroeconomic trends and regulatory developments. The findings highlight the potential of sentiment analysis as a complementary tool for market prediction, offering valuable insights into the emotional undercurrents that shape cryptocurrency markets. This study contributes to a deeper understanding of the socio-economic and psychological dimensions of Bitcoin, providing a foundation for future research in sentiment-driven market analysis.
Bassam W. Aboshosha, M.A. Zayed, Hany S. Khalifa, Rabie Α. Ramadan
Abstract Background The rapid expansion of Internet of Things applications in healthcare has created new opportunities for improving patient care through real-time monitoring and data sharing. However, this growth also introduces significant challenges related to data security, privacy, and system efficiency, especially for devices with limited processing power and energy resources. To address these issues, this study introduces a blockchain-based lightweight hashing system specifically designed for healthcare environments with resource-constrained devices. The goal is to ensure secure, efficient, and scalable handling of sensitive medical data without overwhelming the capabilities of connected devices. Results The proposed system combines a collision-resistant, lightweight hash function with blockchain technology to enhance data integrity, authentication, and privacy. The hash function minimizes computational demands, making it ideal for wearable and embedded healthcare devices. Blockchain integration enables decentralized data management, preventing unauthorized access and tampering. The system generates unique, immutable patient identifiers and protects electronic health information from common security threats, including collision attacks, Sybil attacks, and cryptographic analysis. Simulation results show improved computational efficiency, lower latency, and effective handling of high transaction volumes with minimal resource usage. Conclusions This research presents a secure and efficient framework for managing medical data in healthcare Internet of Things applications. By leveraging lightweight cryptographic techniques and decentralized data structures, the system addresses key limitations in current solutions while supporting scalability and real-world deployment. Potential applications include secure patient monitoring, real-time sharing of health data, and decentralized management of medical records. The proposed approach provides a foundation for future advancements in digital healthcare systems, particularly in remote care, emergency response, and wearable health technologies.
The prosperity of Ethereum has led to a rise in phishing scams. Initially, scammers lured users into transferring or granting tokens to Externally Owned Accounts (EOAs). Now, they have shifted to deploying phishing contracts to deceive users. Specifically, scammers trick victims into either directly transferring tokens to phishing contracts or granting these contracts control over their tokens. Our research reveals that phishing contracts have resulted in significant financial losses for users. While several studies have explored cybercrime on Ethereum, to the best of our knowledge, the understanding of phishing contracts is still limited. In this paper, we present the first empirical study of phishing contracts on Ethereum. We first build a sample dataset including 790 reported phishing contracts, based on which we uncover the key features of phishing contracts. Then, we propose to collect phishing contracts by identifying suspicious functions from the bytecode and simulating transactions. With this method, we have built the first large-scale phishing contract dataset on Ethereum, comprising 37,654 phishing contracts deployed between December 29, 2022 and January 1, 2025. Based on the above dataset, we collect phishing transactions and then conduct the measurement from the perspectives of victim accounts, phishing contracts, and deployer accounts. Alarmingly, these phishing contracts have launched 211,319 phishing transactions, leading to 190.7 million in losses for 171,984 victim accounts. Moreover, we identify a large-scale phishing group deploying 85.7% of all phishing contracts, and it remains active at present. Our work aims to serve as a valuable reference in combating phishing contracts and protecting users' assets.
Ever since its inception in 2008, Blockchain technology has been widely used in most industries to ensure data security and authenticity. From Bitcoin to Blockchain-as-a-Service (BaaS), it has been increasingly adopted. Counterfeiting is one of the biggest issues that companies are fighting, impacting revenues, brand value, and customer trust. In this review, a decentralized Blockchain- based supply chain solution to ensure product authenticity without third-party dependency is discussed. Through the use of distributed ledger technology, authentic and fake products can be identified at all levels. Unique QR codes, produced by the system proposed here with SHA-256, provide transparency and traceability to each product. Blockchain-based anticounterfeiting mechanism provides a secure, tamper-evident method of proving product authenticity, allowing organizations to uphold integrity in their supply chain.