•We propose a novel approach to detect illicit accounts on the Ethereum blockchain.•It uses an XGBoost classifier which takes as input 42 features.•Experiments were carried out on a new data set of ...4681 accounts (2179 are illicit).•Insights are provided on the importance of all involved features.•We publish the new data set as a benchmark for future work.
The recent technological advent of cryptocurrencies and their respective benefits have been shrouded with a number of illegal activities operating over the network such as money laundering, bribery, phishing, fraud, among others. In this work we focus on the Ethereum network, which has seen over 400 million transactions since its inception. Using 2179 accounts flagged by the Ethereum community for their illegal activity coupled with 2502 normal accounts, we seek to detect illicit accounts based on their transaction history using the XGBoost classifier. Using 10 fold cross-validation, XGBoost achieved an average accuracy of 0.963 ( ± 0.006) with an average AUC of 0.994 ( ± 0.0007). The top three features with the largest impact on the final model output were established to be ‘Time diff between first and last (Mins)’, ‘Total Ether balance’ and ‘Min value received’. Based on the results we conclude that the proposed approach is highly effective in detecting illicit accounts over the Ethereum network. Our contribution is multi-faceted; firstly, we propose an effective method to detect illicit accounts over the Ethereum network; secondly, we provide insights about the most important features; and thirdly, we publish the compiled data set as a benchmark for future related works.
•A Blockchain-based supply chain tracking system using smart contracts and decentralized storage.•Present a framework with the algorithms and sequence diagram detailing stakeholder interactions.•Test ...and validate various scenarios to assess the practicality of the proposed solution.•Present cost and security analysis in implementing the proposed solution.
The COVID-19 pandemic has severely impacted many industries, in particular the healthcare sector exposing systemic vulnerabilities in emergency preparedness, risk mitigation, and supply chain management. A major challenge during the pandemic was related to the increased demand for Personal Protective Equipment (PPE), resulting in critical shortages for healthcare and frontline workers. This is due to the lack of information visibility combined with the inability to precisely track product movement within the supply chain, requiring a robust traceability solution. Blockchain technology is a distributed ledger that ensures a transparent, safe, and secure exchange of data among supply chain stakeholders. The advantages of adopting blockchain technology to manage and track PPE products in the supply chain include decentralized control, security, traceability, and auditable time-stamped transactions. In this paper, we present a blockchain-based approach using smart contracts to transform PPE supply chain operations. We propose a generic framework using Ethereum smart contracts and decentralized storage systems to automate the processes and information exchange and present detailed algorithms that capture the interactions among supply chain stakeholders. The smart contract code was developed and tested in Remix environment, and the code is made publicly available on Github. We present detailed cost and security analysis incurred by the stakeholders in the supply chain. Adopting a blockchain-based solution for PPE supply chains is economically viable and provides a streamlined, secure, trusted, and transparent mode of communication among various stakeholders.
Decentralized exchanges (DEXs) allow parties to participate in financial markets while retaining full custody of their funds. However, the transparency of blockchain-based DEX in combination with the ...latency for transactions to be processed, makes market-manipulation feasible. For instance, adversaries could perform front-running - the practice of exploiting (typically non-public) information that may change the price of an asset for financial gain.In this work we formalize, analytically exposit and empirically evaluate an augmented variant of front-running: sandwich attacks, which involve front- and back-running victim transactions on a blockchain-based DEX. We quantify the probability of an adversarial trader being able to undertake the attack, based on the relative positioning of a transaction within a blockchain block. We find that a single adversarial trader can earn a daily revenue of over several thousand USD when performing sandwich attacks on one particular DEX - Uniswap, an exchange with over 5M USD daily trading volume by June 2020. In addition to a single-adversary game, we simulate the outcome of sandwich attacks under multiple competing adversaries, to account for the real-world trading environment.
Blockchain applications in drug data records Jaya, Robert Muliawan; Rakkhitta, Valentino Dhamma; Sembiring, Pranata ...
Procedia computer science,
2023, Letnik:
216
Journal Article
Recenzirano
Odprti dostop
Blockchain is a data storage technique in the form of blocks where the hash system and blocks that cannot be manipulated make blockchain suitable for storing important data. One of them is drug data, ...where currently in drug data, data manipulation often occurs which leads to drug counterfeiting. From previous research, blockchain in the medical world has been applied in storing patient history by utilizing smart contracts. Goals of blockchain application, drug data from manufacturers can be directly viewed and purchased by buyers. The research more towards innovation, blockchain as a database for storing drug data. Ethereum blockchain can be implemented and stored data can be well integrated with Smart Contract. The existence of smart contracts that support and facilitate transactions between producers and buyers. To maintain data security, system will be implementing access permissions in smart contracts to maintain data integrity and security. Eventually maintaining privacy, decentralization, transparency, and authentication in drug data and every drug transaction can be implemented properly.
This article analyzes specific characteristics of value created through digital scarcity and blockchain-proven ownership in cryptogames. Our object of study is CryptoKitties, the first instance of a ...blockchain-based game that has garnered media recognition and financial interest. The objective of this article is to demonstrate the limits of scarcity in value construction for owners of CryptoKitties tokens, manifested as breedable virtual cats. Our work extends the trends set out by earlier cryptocurrency studies from the perspective of cultural studies. For the purpose of this article, we rely on open blockchain analytics such as DappRadar and Etherscan, as well as player-created analytics, backed by a one-year-long participant observation period in the said game for research material. Combining theoretical cryptocurrency and Bitcoin studies, open data analysis, and virtual ethnography enables a grounded discussion on blockchain-based game design and play.
Understanding Ethereum via Graph Analysis Chen, Ting; Li, Zihao; Zhu, Yuxiao ...
ACM transactions on Internet technology,
05/2020, Letnik:
20, Številka:
2
Journal Article
Recenzirano
Ethereum, a blockchain, supports its own cryptocurrency named Ether and smart contracts. Although more than 8M smart contracts have been deployed on Ethereum, little is known about the ...characteristics of its users, smart contracts, and the relationships among them. We conduct the first systematic study on Ethereum by leveraging graph analysis to characterize three major activities on Ethereum, namely money transfer, smart contract creation, and smart contract invocation. We collect all transaction data, construct three graphs from the data to characterize major activities via graph analysis, and discover new insights. Moreover, we address three security issues based on graphs.
Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare ...professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have witnessed billions of sensors, devices, and vehicles being connected through the Internet. One such technology-remote patient monitoring-is common nowadays for the treatment and care of patients. However, these technologies also pose grave privacy risks and security concerns about the data transfer and the logging of data transactions. These security and privacy problems of medical data could result from a delay in treatment progress, even endangering the patient's life. We propose the use of a blockchain to provide secure management and analysis of healthcare big data. However, blockchains are computationally expensive, demand high bandwidth and extra computational power, and are therefore not completely suitable for most resource-constrained IoT devices meant for smart cities. In this work, we try to resolve the above-mentioned issues of using blockchain with IoT devices. We propose a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network. These additional privacy and security properties in our model are based on advanced cryptographic primitives. The solutions given here make IoT application data and transactions more secure and anonymous over a blockchain-based network.
Recently, blockchain technology has become a topic in the spotlight but also a hotbed of various cybercrimes. Among them, phishing scams on blockchain have been found to make a notable amount of ...money, thus emerging as a serious threat to the trading security of the blockchain ecosystem. In order to create a favorable environment for investment, an effective method for detecting phishing scams is urgently needed in the blockchain ecosystem. To this end, this article proposes an approach to detect phishing scams on Ethereum by mining its transaction records. Specifically, we first crawl the labeled phishing addresses from two authorized websites and reconstruct the transaction network according to the collected transaction records. Then, by taking the transaction amount and timestamp into consideration, we propose a novel network embedding algorithm called trans2vec to extract the features of the addresses for subsequent phishing identification. Finally, we adopt the one-class support vector machine (SVM) to classify the nodes into normal and phishing ones. Experimental results demonstrate that the phishing detection method works effectively on Ethereum, and indicate the efficacy of trans2vec over existing state-of-the-art algorithms on feature extraction for transaction networks. This work is the first investigation on phishing detection on Ethereum via network embedding and provides insights into how features of large-scale transaction networks can be embedded.
•From the network science perspective, the Ethereum transactional behaviors such as the transaction volume, the transaction relation and the component structure of the graph share a similarity in ...that they exhibit the heavy-tail properties and could be approximated statistically by the power law function.•This research is the first work that provides a relatively comprehensive investigation into the transaction data recorded in the Ethereum blockchain and tries to find the statistical laws of the data from the perspective of network science.•Experiments also indicate that the transaction network exhibits a bow-tie structure with negative assortativity if it is seen as a directed network.
In recent years, the rapid development of blockchain technologies has attracted considerable attention. However, little effort has been devoted toward investigating the large amount of trade data recorded in blockchains. This paper focuses on transaction data in Ethereum, which is a prominent public blockchain platform supporting not only secure cryptocurrency transfer but also various decentralized applications. By means of the framework of network science theory, we find that several transaction features, such as transaction volume, transaction relation, and component structure, exhibit a heavy-tailed property and can be approximated by the power law function. In particular, we find that the transaction relations follow a bow-tie structure with negative assortativity if they are regarded as a directed graph. The popular hubs tend to connect to a large number of common users. We believe that the aforementioned statistics can be ascribed to the vast diversity of transactions and the existence of a number of cryptocurrency exchanges. To the best of our knowledge, this study is the first to not only carry out a relatively comprehensive investigation of the transaction data recorded in Ethereum but also probe the statistical laws underlying the transaction relationships from the perspective of network science.
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