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824 results · page 9 of 35
Bahareh Parhizkari, Antonio Ken Iannillo, Christof Ferreira Torres, Sebastian Bănescu · 6 authors
No abstract is available for this record.
Dorottya Zelenyanszki, Zhé Hóu, Kamanashis Biswas, Vallipuram Muthukkumarasamy
Non-fungible tokens (NFTs) are unique tokens with various domains, e.g. real estate, metaverse, gaming and public auctions. However, when minted on public blockchains, the underlying blockchain transaction data can be publicly accessible. This instigated transaction data analysis for various purposes, including cryptocurrency price prediction and NFT market analysis. The public data may be considered privacy-sensitive which sets a barrier to the wider adoption of NFTs. In this work, we present that the analysis of the transaction events can describe activities in NFT applications by establishing connections between transactions and thereby, it can identify information that may be privacy-sensitive. This can be useful in developing suitable privacy-enhancing methods for NFTs. We collected transaction data from a blockchain-based game called Planet IX that was built on the Polygon blockchain and used graph visualisation to provide examples for constructed connections.
Kelsie Nabben, Primavera De Filippi
No abstract is available for this record.
Heesang Kim, Dohoon Kim
In the burgeoning landscape of blockchain technologies, the quest for a robust and comprehensive evaluation framework remains an exigent challenge. This study introduces an unprecedented methodology that synergizes the common vulnerability scoring system (CVSS) and the weighting mechanism to evaluate blockchain platforms across the multiple dimensions of mainnet, fungible tokens, and non-fungible tokens, using miscellaneous criteria such as legal compliance and proprietary technology. CVSS is employed to perform a quantitative assessment of blockchain-specific vulnerabilities across various sub-factors, thereby establishing an empirical foundation. Subsequently, the weighting mechanism is used to effectively translating qualitative insights into quantifiable metrics. The introduction of this methodological framework is particularly timely and necessary, given the rapidly evolving landscape of blockchain technology. It promises to standardize the evaluation process, providing a robust foundation for future research, policy-making, and technological advancements in the blockchain domain. In doing so, our study not only fills a critical gap in the current literature but also paves the way for a more systematic and informed approach to blockchain assessment. This dual-tiered approach not only ensures a balanced evaluation, capturing both mechanistic intricacies and human subjectivity, but also renders a highly adaptable and scalable framework. The proposed methodology is anticipated to significantly contribute to the standardization of blockchain evaluation, thereby fostering informed decision-making among stakeholders and catalyzing advancements in the blockchain ecosystem.
Huma Zafar
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).
Oumaima Fadi, Adil Bahaj, Karim Zkik, Abdellatif El Ghazi · 6 authors
No abstract is available for this record.
Junha Kang, Seok-Jun Buu
As the popularity of cryptocurrencies grows, the threat of phishing scams on trading networks is growing. Detecting unusual transactions within the complex structure of these transaction graphs and imbalanced data between Benign and Scams remains a very important task. In this paper, we present Disentangled Prototypical Graph Convolutional Autoencoder, which is optimized for detecting anomalies in cryptocurrency transactions. Our model redefines the approach to analyzing cryptocurrency transactions by treating them as edges and accounts as nodes within a graph neural network enhanced by autoencoders. The DP-GCAE model differentiates itself from existing models by implementing disentangled representation learning within its autoencoder framework. This innovative approach allows for a more nuanced capture of the complex interactions within Ethereum transaction graphs, significantly enhancing the ability of the model to discern subtle patterns often obscured in imbalanced datasets. Building upon this, the autoencoder employs a triplet network to effectively disentangle and reconstruct the graph. Reconstruction is used as input to Graph Convolutional Network to detect unusual patterns through prototyping. In experiments conducted on real Ethereum transaction data, our proposed DP-GCAE model showed remarkable performance improvements. Compared with existing graph convolution methods, the DP-GCAE model achieved a 37.7 percent point increase in F1 score, validating the effectiveness and importance of incorporating disentangled learning approaches in graph anomaly detection. These advances not only improve the F1-score of identifying phishing scams in cryptocurrency networks, but also provide a powerful framework that can be applied to a variety of graph-based anomaly detection tasks.
Zulfiqar Ali Khan, Akbar Siami Namin
Smart Contracts (SCs) communicate with each other using external calls. Their interactions can be malicious, resulting in the loss of Ether. One can blame the reentrancy attack for this exploitation. Several previous endeavors detected the reentrancy vulnerability by creating testing tools using static analysis like Remix. However, these approaches do not execute the programs; hence, we cannot confirm their results. In this paper, we present TechyTech that detects both reentrancy and tx.origin vulnerabilities using a novel dynamic analysis approach of involuntary transfer (i.e., unintended transfer). Henceforth, we use a tree-based categorization string to distinguish the two vulnerabilities and their variations. Further, our research discusses multiple SC-related issues like the hijacked stack, deployed owner, and non-generation of transaction receipts in connection with reentrant calls, which we could not find in previous work. Using an example, we demonstrate how the actual Ether transfer is greater than the intended due to reentrancy.We acknowledge that due to dynamic analysis, TechyTech may suffer from VMExceptions.
S. B. Indra, S. Harshini, R. Sabitha
No abstract is available for this record.
Hao Sui, Jiale Zhang, Bing Chen, Di Wu · 6 authors
No abstract is available for this record.
Lejun Zhang, Junjie Zhang, Kentaroh Toyoda, Yuan Liu · 7 authors
Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges, gambling, marketplaces, and also scams such as high-yield investment projects. Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly. Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way. In this paper, we address the problem of identifying multiple classes of Bitcoin services, and for the poor classification of individual addresses that do not have significant features, we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities. The innovation of the method is to (1) Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task. (2) Unlike the general supervised model approach, this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships. Specifically, after obtaining the overall features, the address classification and entity clustering tasks are performed separately, and the results are subjected to graph-based maximization consensus. The final result is made to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible. By testing and evaluating over 26,000 Bitcoin addresses, our feature extraction method captures more useful features. In addition, the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.
Istiaque Ahmed, Kai Fumimoto, Tadashi Nakano, Thi Hong Tran
The charity sector impacts society significantly in many areas, including providing education, healthcare, hunger relief, drinking water, disaster relief, environmental preservation, and assistance to underserved people. The existing charity organizations have numerous limitations, such as poor management, high operation costs, and a lack of transparency in the donation execution flow. The authentication of users and institutions is a big problem in the existing system. This research resolves the issues of transparency and reliability with an immutable and traceable distributed ledger. We empower the existing centralized charity works with the electronic know-your-customer (eKYC) authentication approach and cryptographic HASH. Information privacy is implemented using the filters within smart contracts. The implementation of eKYC to ensure authenticity and to secure data flow through the channel are two significant contributions of this work. A coin-toss function for data selection and a random time delay between pieces of data are used to avoid attacks based on guesswork. We aim for this framework to send 100% of donations to the beneficiaries and become a hyper-liquid medium to fill the United Nations Sustainable Development Goals (SDG) funding gap. We also introduce the concept of service charity to broaden the ability for people to offer their services and skills as charity.
Momna Akhtar, Muhammad Rahim, Agaeb Mahal Alanzi, Sadique Ahmad · 7 authors
This paper presents novel operational laws forp,q,r–spherical fuzzy sets (p,q,r–SFSs) by harnessing the Dombi t-norm (DTN) and t-conorm (DTCN). These laws serve as the foundation for a set of aggregation operators (AOs) designed to consolidatep,q,r–spherical fuzzy (p,q,r–SF) information. Additionally, a multi-criteria decision-making (MCDM) method is outlined for addressing practical decision-making (DM) challenges. To demonstrate the application of the proposed approach, a numerical example is offered. Furthermore, we conducted a comparative study to validate the efficacy of the suggested approach. Finally, we discuss both the advantages and limitations of this innovative approach.
Yanhua Liu, Zhihuang Liu, Qiu Zhang, Jinshu Su · 6 authors
Blockchain-based healthcare IoT technology research enhances security for smart healthcare services such as real-time monitoring and remote disease diagnosis. To incentivize positive behavior among participants within a blockchain-based smart healthcare system, existing efforts employ benefit distribution and reputation assessment methods to enhance performance. Yet, there remains a significant gap in multidimensional assessment strategies and consensus improvements in addressing complex healthcare scenarios. In this paper, we propose a blockchain and trusted reputation assessment-based incentive mechanism for healthcare services (BtRaI). BtRaI provides a realistic and comprehensive reputation assessment with feedback to motivate blockchain consensus node participation, thus effectively defending against malicious behavior in the healthcare service system. Specifically, BtRaI first introduces multiple moderation factors for comprehensive multidimensional reputation assessment and credibly records the assessment results on the blockchain. Then, we propose an improved PBFT algorithm, grounded in the reputation assessment, to augment blockchain consensus efficiency. Finally, BtRaI designs a token-based reward and punishment mechanism to motivate honest participation in the blockchain, inhibit potential misbehavior, and promote enhanced service quality in the healthcare system. Theoretical analysis and simulation experiments conducted across various scenarios demonstrate that BtRaI effectively suppresses malicious attacks in healthcare services , improves blockchain node fault tolerance rates, and achieves blockchain transaction processing efficiency within 0.5 s in a 100-node consortium chain. BtRaI’s reputation assessment and token incentive mechanism, characterized by realistic differentiation granularity and change curves, are well-suited for dynamic and complex healthcare service environments.
Daniela Pöhn, Michael Grabatin, Wolfgang Hommel
Self-sovereign identity (SSI) is a digital identity management model managed in a decentralized manner. It allows identity owners to manage and store their digital identities in a software wallet, for example, on a smartphone, without relying on centralized providers. This approach tries to enhance the security and privacy of digital identities and, thereby, their owners. With the new eIDAS regulation, elements of SSI, such as the wallet, are being pushed onto the market. However, since the model is relatively new, the security threats are still not fully known. This is shown by a brief security analysis of selected existing SSI wallets. In order to get a picture of the known threats, we systematically analyze and categorize related work in the field of SSI and elements applied by SSI. We then evaluate their application to current SSI systems and identify future work.
Shahnawaz Khan
Zakat is one of the five mandatory pillars of Islam. It plays a vital role in addressing the social issues such as wealth inequality and supporting the less fortunate. Any Muslim whose wealth and assets exceeds the Nisab value must pay zakat. However, unlike taxes, zakat is not collected by government or authorities. Therefore, every eligible individual is responsible for its calculation and distribution independently. Hence, there are several issues and challenges associated with its collection and distribution, especially in non-Islamic countries. This research paper proposes a blockchain-based online platform for zakat collection and distribution, addressing these challenges. Blockchain can help to ensure that zakat funds are distributed to beneficiaries in an efficient manner. The system allows zakat payers to select and allocate funds to the verified beneficiaries while ensuring Sharia compliance. The proposed system uses a blockchain ledger to enhances transparency, and accountability. It minimizes the associated collection and distribution costs by removing intermediaries. This research aims to contribute to the efficient and secure management of zakat funds, promoting social justice, and responsible zakat practices in the Muslim community.
Chibuzo Obi-Okoli, Olamide Jogunola, Bamidele Adebisi, Mohammad Ali A. Hammoudeh
The rapid growth and psudonomity inherent in blockchain technology such as in Bitcoin and Ethereum has marred its original intent to reduce dependant on centralised system, but created an avenue for illicit activities, including fraud, phishing, scams, etc. This undermines the reputation of blockchain network, giving rise to the need to identify these illicit activities within the blockchain network. This current work tackles this crucial problem by investigating and implementing six machine learning algorithms with a particular emphasis on striking a balance between accuracy, precision and recall. The novelty of the work lies in the utilising of the synthetic minority over-sampling technique to handle data imbalance. Thus, increasing the accuracy of the light gradient boosting machine classifier to 98.4%. The outcome of this work holds great potential for enhancing the security and credibility of blockchain ecosystems paving the way for a more secure and dependable digital future in the age of decentralised and trustless systems.
Hou-Wan Long, Xiongfei Zhao, Yain‐Whar Si
Decentralized Finance (DeFi), propelled by Blockchain technology, has revolutionized traditional financial systems, improving transparency, reducing costs, and fostering financial inclusion. However, transaction activities i n these systems fluctuate significantly and the throughput can be effected. To address this issue, we propose a Dynamic Mining Interval (DMI) mechanism that adjusts mining intervals in response to block size and trading volume to enhance the transaction throughput of Blockchain platforms. Besides, in the context of public Blockchains such as Bitcoin, Ethereum, and Litecoin, a shift towards transaction fees dominance over coin-based rewards is projected in near future. As a result, the ecosystem continues to face threats from deviant mining activities such as Undercutting Attacks, Selfish Mining, and Pool Hopping, among others. In recent years, Dynamic Transaction Storage (DTS) strategies were proposed to allocate transactions dynamically based on fees thereby stabilizing block incentives. However, DTS’ utilization of Merkle tree leaf nodes can reduce system throughput. To alleviate this problem, in this paper, we propose an approach for combining DMI and DTS. Besides, we also discuss the DMI selection mechanism for adjusting mining intervals based on various factors.
Zhiju Yang, Gaoyuan Man, Songqing Yue
As decentralized finance (DeFi) built on blockchain grows rapidly, the security of smart contracts underpinning DeFi has become a major concern due to exploits leading to billions in damages. Although tools exist for automated vulnerability detection in smart contracts, studies show that most vulnerabilities remain undetected. In this work, we propose using fine-tuned large language models (LLMs) for enhanced automated detection of vulnerabilities in smart contracts. We collected over 26,727 labeled smart contract vulnerabilities and fine-tuned the 13B parameter Llama-2 model. Evaluation of 1,000 unseen functions shows promising precision of 31-36% in predicting vulnerability categories. The fine-tuned LLM demonstrates potential as an auxiliary tool to identify vulnerable code and assist auditors. Future work is outlined for improving performance via larger models, higher-quality data, and specialized binary detection models. We present promising preliminary results on integrating LLMs into smart contract analysis and motivate further research at the intersection of LLMs and blockchain security.
Subhadeep Mandal, Arpan Kumar Kar, Shivam Gupta
No abstract is available for this record.
Kevin K.W. Ho, Dickson K.W. Chiu, C. H. Au, Francis Dalisay · 6 authors
This position article summarizes the panelists’ presentations and discussions at the panel “Fake News, Misinformation, and Privacy: How COVID-19 Pandemic Changed Our Society,” held at the 15th International Conference on Information Resources Management (Conf-IRM 2022) on October 18, 2022. The panel discussed their views on (1) how to stop the spreading of health misinformation; (2) how information sources affect online health information behavior; (3) how news literacy increases people's desire to seek out information by increasing their skepticism; and (4) how political beliefs, trust, and privacy concerns affect people's decisions during COVID-19. This article also discusses how blockchain and distributed ledger technologies can help tackle the fake news and misinformation problem.
B. Hong, Thắng Lê Đức, Doan Minh Trung, Tuan-Dung Tran · 6 authors
The proliferation of smart contracts on blockchain technology has led to several security vulnerabilities, causing significant financial losses and instability in the contract layer. Existing machine learning-based static analysis tools have limited detection accuracy, even for known vulnerabilities. In this study, we propose a novel deep learning-based model combined with attention mechanisms for identifying security vulnerabilities in smart contracts. Our experiments on two large datasets (SmartBugs Wild and Slither Audited Smart Contracts) demonstrate that our approach successfully achieves a 90% detection accuracy in identifying smart contract reentrancy attacks (e.g. performing better than other existing state-of-the-art deep learning-based approaches). In addition, this work also establishes the practical application of deep learning-based technology in smart contract reentrancy vulnerability detection, which can promote future research in this domain.
Mutlu Tahsin Üstündağ, Gizem Yıldız, Mustafa TANRIVERDİ, Togay Seçkin BİRBUDAK
In this study, it is aimed to analyze the articles on Web3 and present the general situation about Web3 to researchers. Within the scope of this purpose, the trends of the studies published on Web3 according to years, the trends of the journals in which they were published, the institutions and countries that contributed the most, the keywords used in the studies, the topics and themes based on the studies, and the distribution of research areas were revealed. The research is based on bibliometric analysis. A total of 280 articles published in WoS and SCOPUS databases were analyzed. WoSViewer and Bibliometrix programs were used in data analysis. The findings were analyzed and interpreted separately in WoS and SCOPUS. As a result of the research, there was a significant increase in studies on Web3 in 2022, and the journals with the highest number of publications in WoS and SCOPUS differ. The countries that contributed the most to Web3 were China, The USA, India, England, Germany. The most cited countries are China, the USA, India, England, Iran and Canada. In general, it can be said that countries and institutions have conducted studies on Web3 by addressing many issues related to Web3. Within the scope of the results, Web3 studies address many different disciplines with many topics. However, there is a need to deepen the studies. The policies, practices and even the laws created by countries on Web3 are important for studies on Web3. Blockchain is one of the most studied topics, but it is understood that there are some hesitations about blockchain security. For this reason, Web3 studies can be conducted to increase blockchain security.
Sijia Li, Gaopeng Gou, Chang Liu, Gang Xiong · 7 authors
Phishing scams have become the most serious type of crime involved in Ethereum. However, existing methods ignore the natural camouflage and sparse distribution of phishing scams in Ethereum leading to unsatisfactory performance, and they are also limited by the data scale which cannot be applied to real-world dynamic scenarios. In this paper, we propose a Transaction Graph Contrast network (TGC) to enhance phishing scam detection performance on Ethereum. TGC inputs subgraphs instead of the entire graph for training, which eases the model’s requirements for machine configuration and data connectivity. Motivated by phishing nodes are surrounded by normal nodes, we design the comparison between node-level to help phishing nodes learn the unique properties of themselves different from their neighbors. Observing the small number and sparse distribution of phishing nodes, we narrow the distance between phishing nodes by comparing node context-level structures, so as to learn universal transaction patterns. We further combine the obtained features with common statistics to identify phishing addresses. Evaluated on real-world Ethereum phishing scams datasets, our TGC outperforms the state-of-the-art methods in detecting phishing addresses and has obvious advantages in large-scale and dynamic scenarios.