Follow blockchain research across journals, conferences, and preprint repositories.
1,600 results · page 16 of 67
Ye Qiao, Guang Li, Jieying Zhou, Weigang Wu
No abstract is available for this record.
Suresh Kumar Kagitha, Diksha Rani, Pavan Kumar Penta
Cryptocurrency tracker is an online platform that provides a userfriendly experience. Users get a simple and userfriendly experien ce through the user interface. Users can sign into their account with Gmail or a mobile number for easy access to their account. U sers can track prices of different cryptocurrencies and view currency charts. Using this user interface, users can find prices and ot her relevant information about cryptocurrencies. The app helps users to create watchlists and we can track prices. We can set alerts for cryptocurrency prices. We can customize notifications and help understand new cryptocurrency trends. Users can easily find various cryptocurrencies and track future crypt currency trends. It helps users invest in new popular cryptocurrencies that will be more useful to them in the future. Overall, the Cryptocurrency Tracker web app is a valuable tool for anyone looking to invest, trade, or just keep an eye on the cryptocurrency market. It provides realtime data and insights that can help users make informed investment decisions and stay abrea st of the latest industry trends and developments.
Saurabh Singh, Kanshu Sharma, Richa Jain
No abstract is available for this record.
Rodney Garratt, Maarten R.C. van Oordt
No abstract is available for this record.
Yuhang Zhang, Yanjing Lu, Mian Li
No abstract is available for this record.
Divya Rishi Sahu, Harsh Tiwari, Deepak Singh Tomar, R. K. Pateriya
No abstract is available for this record.
Hu Yuhao, Peng Su
Addressing the shortcomings of traditional voting systems, such as fraud, lack of transparency, and inefficiency, this paper proposes a decentralized voting model based on the Polygon blockchain. The model leverages the inherent characteristics of blockchain, including anti-tampering, transparency, and security, to establish a secure and trustworthy voting mechanism. By utilizing smart contract, the model automates voting, enforces rules, records ballots in real-time, and prevents double voting, thereby significantly reduces human errors and election fraud. It also ensures privacy, eliminates coercion, and realizes on-chain tallying using the Paillier homomorphic encryption. The model is analyzed in terms of security and deployed on Polygon, a sidechain of Ethereum. Comparisons with other schemes demonstrate that this model is also optimized for reducing gas cost.
Hironori Uchibori, Katsunari Yoshioka, Kazumasa Omote
In recent years, the convenience and potential use of crypto-assets such as Bitcoin and Ethereum have attracted increasing attention. On the other hand, there have been reports of attacks on the blockchain networks that support crypto-assets in an attempt to steal other users’ assets. In the past, research on attack observation against blockchains has used techniques such as holding real crypto-assets to lure attackers into honeypots or falsifying balances to attackers. However, these methods risk losing crypto-assets to attackers or being exposed as honeypots to attackers. To solve these problems, we propose a new RPC (Remote Procedure Call) honeypot method that returns the wallet address of another partya. holding a high balance in response to an attacker’s request, thereby luring the attacker without having the real crypto-assets. Our experimental evaluation shows that this method can attract more attackers than the method with zero-balance wallets and can observe more sophisticated attacks. Furthermore, we proposed a risk reduction strategy for crypto-asset theft by applying the idea of our method. In the log analysis process, we devised a new clustering method using the number of times an attacker executes a specific method as a feature. By applying this method, we successfully classified attackers based on their objectives, demonstrating the efficient analysis of vast amounts of log data.
Jeyakumar Samantha Tharani, E.Y.A. Charles, Zhé Hóu, Vallipuram Muthukkumarasamy
No abstract is available for this record.
Fred Steinmetz
No abstract is available for this record.
Yueyue He, Jiageng Chen, Koji Inoue
No abstract is available for this record.
Tassos Dimitriou
No abstract is available for this record.
Gurpreet Kour Sodhi, Mekhla Sharma, Rajan Miglani
No abstract is available for this record.
Jeyakumar Samantha Tharani, Zhé Hóu, Eugene Yugarajah Andrew Charles, Punit Rathore · 6 authors
Blockchain technology has been integrated into a wide range of applications in various sectors, such as finance, supply chain, health, and governance. However, the participation of a few actors with malicious intentions challenges law enforcement authorities, regulators and other users. These challenges revolve around dealing with an array of illegal activities such as asset trades in dark markets, receiving payments for cyber-attacks, and facilitating money laundering. Developing an efficient mechanism to identify malicious actors in blockchain networks is a pressing need to build confidence among the stakeholders and ensure regulatory adherence. The raw data of blockchain transactions do not readily reveal the dynamic behavioural changes and their interconnection between transactions and accounts. These behavioural patterns can be useful for identifying malicious actors. Machine Learning (ML)-based models for early warning and/or detection are considered one of the potential approaches. In ML, feature engineering plays a crucial role in enhancing the predictive performance of a model. This study proposes different categories of features and unified feature extraction approaches for raw Bitcoin and Ethereum transaction data and their interconnection information. As far as we are aware, there has been no study that considered a feature engineering approach for identifying malicious activities. The significance of the engineered features was validated against eight classifiers, including Random Forest (RF), XG-boost (XG), Silas, and neural network-based classifiers. The results showed that these features contribute to higher classification accuracy and higher Area Under the Receiver Operating Characteristic Curve (AUC) value for both Bitcoin and Ethereum transactions. This work also analysed the influence of engineered features in classification using the eXplainable Artificial Intelligence (XAI) technique SHapley Additive exPlanations (SHAP) values. The feature importance scores confirmed the significance of the proposed engineered features towards implementing classification models to identify, target and disrupt malicious activities in blockchain networks.
Chi Jiang, Guojin Sun, Jinqing Shen, Binglei Yue · 5 authors
No abstract is available for this record.
Bahareh Parhizkari, Antonio Ken Iannillo, Christof Ferreira Torres, Sebastian Bănescu · 6 authors
No abstract is available for this record.
Hoang Lai
Misleading information, false claims, and fabricated news articles not only misguide readers but also undermine the trustworthiness of the news platforms themselves. The blockchain provides decentralized, immutable data storage and offers a promising solution to prevent censorship on news websites. Compared to traditional news websites, a decentralized application (dApp) offers benefits such as greater stability and resistance to information manipulation. A decentralized web app is harder to attack than centralized servers since the database is stored across a blockchain network. Moreover, blockchain prevents censorship by letting readers check data across all blocks in the Blockchain, which is good for a trustworthy news service. However, the goal of censorship-resistance conflicts with the typical news site requirements to provide a way to update and hide contents. This project aims to provide a solution by keeping all versions of the information from related authors. The project presents a news site that resists censorship. It still allows authors to update previous news stories and to hide inappropriate comments from others, but still retains previous versions of the information on the blockchain, thus ensuring that the content can never be purged from the internet. In addition, I discuss the cost of this website running on Ethereum, and solutions to optimize my design, such as using the Interplanetary File System (IPFS) to reduce the cost.
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.