Abdullah Al Omar, Abu Kaisar Jamil, Amith Khandakar, Abdur Razzak Uzzal · 7 authors
A smart city ensures quality maintenance in diverse sectors, namely citizen safety, security, healthcare, transportation, and energy. Besides, data privacy and security have become an uprising concern for Electronic Health Records (EHR) in smart cities. This is because the EHR platforms are constantly getting cyber threats from cybercriminals. On the other hand, health insurance companies offer certain specific policies that require the association of patients' financial data with EHRs. Thus, additional security concern arises as fraudulent entities can alter these insurance policies. An extra challenge is triggered as patients need to validate their identities separately while communicating with different smart healthcare entities. This is because these healthcare facilities and insurance companies ought to ensure authenticity before offering any service for an individual. Hence, we have implemented a blockchain framework to safeguard patients' personal information and insurance policy. In this paper, we propose a solution for the healthcare system that provides data privacy and transparency. Furthermore, in the proposed system, insurance policies are incorporated in blockchain via the Ethereum platform and data privacy is shielded with cryptographic tools.
Smart contract vulnerability detection draws extensive attention in recent years due to the substantial losses caused by hacker attacks. Existing efforts for contract security analysis heavily rely on rigid rules defined by experts, which are labor-intensive and non-scalable. More importantly, expert-defined rules tend to be error-prone and suffer the inherent risk of being cheated by crafty attackers. Recent researches focus on the symbolic execution and formal analysis of smart contracts for vulnerability detection, yet to achieve a precise and scalable solution. Although several methods have been proposed to detect vulnerabilities in smart contracts, there is still a lack of effort that considers combining expert-defined security patterns with deep neural networks. In this paper, we explore using graph neural networks and expert knowledge for smart contract vulnerability detection. Specifically, we cast the rich control- and data- flow semantics of the source code into a contract graph. To highlight the critical nodes in the graph, we further design a node elimination phase to normalize the graph. Then, we propose a novel temporal message propagation network to extract the graph feature from the normalized graph, and combine the graph feature with designed expert patterns to yield a final detection system. Extensive experiments are conducted on all the smart contracts that have source code in Ethereum and VNT Chain platforms. Empirical results show significant accuracy improvements over the state-of-the-art methods on three types of vulnerabilities, where the detection accuracy of our method reaches 89.15%, 89.02%, and 83.21% for reentrancy, timestamp dependence, and infinite loop vulnerabilities, respectively.
Ilhaam A. Omar, Raja Jayaraman, Mazin Debe, Khaled Salah · 6 authors
Effectively managing the healthcare supply chain (HCSC) process is crucial for healthcare providers not only during pandemics such as COVID-19 but also in their normal operations. Despite significant advances in new technologies and treatment options providers still suffer from poor procurement, ordering, forecasting, and distribution practices. Group Purchasing Organizations (GPOs) are an important stakeholder in HCSC and benefit providers with cost savings, volume discounts, and vendor selection. However, the current GPO contract process is time-consuming and lacks efficiency. Hence, our proposed solution integrates blockchain technology and decentralized storage to promote transparency, streamlines communication with stakeholders, and minimize the procurement timeline while avoiding pricing discrepancies and inaccuracies. Our solution connects all the stakeholders such as manufacturer, GPO, distributor, and provider using Ethereum network. In this paper, we propose a blockchain solution using smart contracts to automate the GPO contract process. We propose a generic framework for contracting process in the HCSC with detailed algorithms depicting various interactions among HCSC stakeholders. The smart contract code was developed and tested using Remix IDE and the code is publicly shared via Github. We discuss various security risks and present detailed cost analysis of various transactions incurred by the stakeholders. Our analysis demonstrates that the proposed blockchain-based solution is economically feasible as only a minimal transaction fee is expended by the stakeholders in the distributed network.
Jesús Correas, Pablo Gordillo, Guillermo Román‐Díez
Profiling tools have been widely used for studying the behavior of the programs with the objective of reducing the amount of resources consumed by them. Most profilers collect the information with dynamic techniques, i.e., execute an instrumented version of the program with some specific input arguments to profile the measures of interest. This article presents a novel static profiling technique for Ethereum smart contracts that, using static resource analysis, is able to generate upper-bound expressions that can be used to produce profiling information about the measure of interest. Unlike traditional profiling tools, we get upper-bounds on the measures of interest expressed in terms of the input arguments or the state variables of the smart contracts. The information that can be obtained by the upper-bounds allows us to detect gas-expensive fragments of a Solidity program or to spot resource-related vulnerabilities at specific program points of the program. Moreover, in this article we propose an automatic optimization of Solidity programs which reduces their gas consumption replacing the accesses to state variables by gas-efficient accesses to local variables. We have experimentally evaluated our technique and we have detected that 6.81% of the public functions analyzed can be optimized and 1.43% are vulnerable to execute arbitrary code.
Nishant Jagannath, Tudor Barbulescu, Karam M. Sallam, Ibrahim Elgendi · 8 authors
The Ethereum blockchain generates a significant amount of data due to its intrinsic transparency and decentralized nature. It is also referred to as on-chain data and is openly accessible to the world. Moreover, the on-chain data is timestamped, integrated, and validated into an open ledger. This important blockchain feature enables us to assess the network’s health and usage. It serves as a massive data warehouse for complex prediction algorithms that can effectively detect systemic trends and forecast future behavior. We adopt a quantitative approach using a subset of these metrics to determine the network’s true monetary value by developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) with the metrics most closely associated with the price as inputs. Since several hyperparameters regulate the learning process in an RNN, they are highly sensitive to their values. It is thus critical, to select optimal hyperparameters so that the training is quick and effective. Determining the optimal parameters of an RNN model is a tedious and complex process. Hence, previous studies have developed several self-adaptive approaches to determine the optimal values for various parameters effectively. However, none of the prior studies explore self-adaptive algorithms in deep learning models in conjunction with on-chain data to predict cryptocurrency prices. In this paper, we propose three self-adaptive techniques, each of which converges on a set of optimal parameters to predict the price of Ethereum accurately. We compare our results to a traditional LSTM model. Our approach exhibits 86.94% accuracy while maintaining a minimum error rate.
Blockchain technology and, in particular, blockchain-based transaction offers us information that has never been seen before in the financial world. In contrast to fiat currencies, transactions through virtual currencies like Bitcoin are completely public. And these transactions of cryptocurrencies are permanently recorded on Blockchain and are available at any time. Therefore, this allows us to build transaction networks (TN) to analyze illegal phenomenons such as phishing scams in blockchain from a network perspective. In this paper, we propose a Transaction SubGraph Network (TSGN) based classification model to identify phishing accounts in Ethereum. Firstly we extract transaction subgraphs for each address and then expand these subgraphs into corresponding TSGNs based on the different mapping mechanisms. We find that TSGNs can provide more potential information to benefit the identification of phishing accounts. Moreover, Directed-TSGNs, by introducing direction attributes, can retain the transaction flow information that captures the significant topological pattern of phishing scams. By comparing with the TSGN, Directed-TSGN indeed has much lower time complexity, benefiting the graph representation learning. Experimental results demonstrate that, combined with network representation algorithms, the TSGN model can capture more features to enhance the classification algorithm and improve phishing nodes' identification accuracy in the Ethereum networks.
Christof Ferreira Torres, Antonio Ken Iannillo, Arthur Gervais, Radu State
In recent years, Ethereum gained tremendously in popularity, growing from a daily transaction average of 10K in January 2016 to an average of 500K in January 2020. Similarly, smart contracts began to carry more value, making them appealing targets for attackers. As a result, they started to become victims of attacks, costing millions of dollars. In response to these attacks, both academia and industry proposed a plethora of tools to scan smart contracts for vulnerabilities before deploying them on the blockchain. However, most of these tools solely focus on detecting vulnerabilities and not attacks, let alone quantifying or tracing the number of stolen assets. In this paper, we present Horus, a framework that empowers the automated detection and investigation of smart contract attacks based on logic-driven and graph-driven analysis of transactions. Horus provides quick means to quantify and trace the flow of stolen assets across the Ethereum blockchain. We perform a large-scale analysis of all the smart contracts deployed on Ethereum until May 2020. We identified 1,888 attacked smart contracts and 8,095 adversarial transactions in the wild. Our investigation shows that the number of attacks did not necessarily decrease over the past few years, but for some vulnerabilities remained constant. Finally, we also demonstrate the practicality of our framework via an in-depth analysis on the recent Uniswap and Lendf.me attacks.
Abstract With the rapid development of blockchain technology and the increasing demand for partial decentralization of the Internet, the application of underlying technology based on blockchain has been widely concerned. Along with decentralized objects, the programmable financial system represented by Ethereum has been gotten more and more attention. However, smart contract sacrifices its security to improve decentralization. So Ethereum has the fatal problem with a large number of users, and negligence of users in coding contract threatens the entire Ethereum network. Therefore, this paper aims to research and expand applications of smart contract usage in Ethereum blockchain. We start from basic concepts to define structure of Ethereum, and then discuss security issues on smart contract. In the end, an optimized smart contract application of auction is implemented, which is useful for further consolidating and understanding of smart contract in practice.
Smart contracts are dependent on oracle systems for their adoption and usability. We perform an empirical study of oracle systems' usage trends and adoption metrics to provide better insight into the health of the smart contract ecosystem. We collect ChainLink usage data on the Ethereum network using a modified Ethereum client and running a full node. We analyze the collected data and present our findings and insights surrounding the usage trends, adoption metrics, oracle pricing and service quality associated with ChainLink on the Ethereum network.
Christof Ferreira Torres, Ramiro Daniel Camino, Radu State
Ethereum prospered the inception of a plethora of smart contract applications, ranging from gambling games to decentralized finance. However, Ethereum is also considered a highly adversarial environment, where vulnerable smart contracts will eventually be exploited. Recently, Ethereum's pool of pending transaction has become a far more aggressive environment. In the hope of making some profit, attackers continuously monitor the transaction pool and try to frontrun their victims' transactions by either displacing or suppressing them, or strategically inserting their transactions. This paper aims to shed some light into what is known as a dark forest and uncover these predators' actions. We present a methodology to efficiently measure the three types of frontrunning: displacement, insertion, and suppression. We perform a large-scale analysis on more than 11M blocks and identify almost 200K attacks with an accumulated profit of 18.41M USD for the attackers, providing evidence that frontrunning is both, lucrative and a prevalent issue.
Shanqing Yu, Jie Jin, Yunyi Xie, Jie Shen · 5 authors
With the rapid growth of blockchain, an increasing number of users have been attracted and many implementations have been refreshed in different fields. Especially in the cryptocurrency investment field, blockchain technology has shown vigorous vitality. However, along with the rise of online business, numerous fraudulent activities, e.g., money laundering, bribery, phishing, and others, emerge as the main threat to trading security. Due to the openness of Ethereum, researchers can easily access Ethereum transaction records and smart contracts, which brings unprecedented opportunities for Ethereum scams detection and analysis. This paper mainly focuses on the Ponzi scheme, a typical fraud, which has caused large property damage to the users in Ethereum. By verifying Ponzi contracts to maintain Ethereum's sustainable development, we model Ponzi scheme identification and detection as a node classification task. In this paper, we first collect target contracts' transactions to establish transaction networks and propose a detecting model based on graph convolutional network (GCN) to precisely distinguishPonzi contracts. Experiments on different real-world Ethereum datasets demonstrate that our proposed model has promising results compared with general machine learning methods to detect Ponzi schemes.
The use of advanced technologies has increased drastically to maintain any sensitive records related to education, health, or finance. It helps to protect the data from unauthorized access by attackers. However, all the existing advanced technologies face some issues because of their uncertainties. These technologies have some lapses to provide privacy, attack-free, transparency, reliability, and flexibility. These characteristics are essential while managing any sensitive data like educational certificates or medical certificates. Hence, we designed an Industry 5.0 based blockchain application to manage medical certificates using Remix Ethereum blockchain in this paper. This application also employs a distributed application (DApp) that uses a test RPC-based Ethereum blockchain and user expert system as a knowledge agent. The main strength of this work is the maintenance of existing certificates over a blockchain with the creation of new certificates that use logistic Map encryption cipher on existing medical certificates while uploading into the blockchain. This application helps to quickly analyze the birth, death, and sick rate as per certain features like location and year.
The market for non-fungible tokens (NFTs), transferrable and unique digital assets on public blockchains, has received widespread attention and experienced strong growth since early 2021. This study provides an introduction to NFTs and explores the 14 largest submarkets using data from the Ethereum blockchain between June 2017 and May 2021. The analyses rely on (a) the number of NFT sales, (b) the dollar volume of NFT trades and (c) the number of unique blockchain wallets that traded NFTs. Based on the number of transactions and wallets, the Ethereum-based NFT market peaked at the end of 2017 due to the success of the CryptoKitties project. As of 2021, fewer transactions occur but the traded value is much higher. We find that NFT submarkets are cointegrated and feature various causal short-run connections between them. The success or adoption of younger NFT projects is influenced by that of more established markets. At the same time, the success of newer markets has an impact on the more established projects. The results contribute to the overall understanding of the NFT phenomenon as an emerging asset class and suggest that NFT markets are immature or even inefficient.
Non-fungible tokens (NFTs) are transferrable rights to digital assets, such as art, in-game items, collectables, or music. The phenomenon and its markets have grown significantly since early 2021. We investigate the interrelationships between NFT sales, NFT users (unique active blockchain wallets), and the pricing of Bitcoin (BTC) and Ether (ETH). Using daily data between January 2018 and April 2021, we show that a Bitcoin price shock triggers an increase in NFT sales. Also, Ether price shocks reduce the number of active NFT wallets. The results suggest that (larger) cryptocurrency markets affect the growth and development of the (smaller) NFT market, but there is no reverse effect.
Naipeng Dong, Babu Pillai, Guangdong Bai, Mark Utting
Abstract Distributed ledger technology (DLT) emerged as a disruptive force towards decentralization and has expanded beyond its origins in cryptocurrencies like Bitcoin. At the heart of DLT is an infrastructure that replicates data across multiple network nodes, enabling new opportunities for data integrity, transparency, and trust in distributed business environments. In recent years, technological advances have improved the performance, energy efficiency, and functionality of DLT, expanding its application to various sectors such as finance, healthcare, trade and media, logistics, and the public sector. Despite these advances, adoption remained limited, with notable successes primarily in areas such as decentralized finance and non-fungible tokens. By placing DLT within the historical development of ledgers and distributed databases, this Fundamental provides a business-oriented foundation for structuring and assessing DLT-based solutions. It presents, a unified definition covering blockchain technologies, describes the key characteristics of DLT, and offers a structured analysis of its potential and challenges using a multi-dimensional interaction framework. Ultimately, it serves to carve out where and under which conditions DLT infrastructures add value for interorganizational relationships.
Ahmad Musamih, Khaled Salah, Raja Jayaraman, Junaid Arshad · 7 authors
Healthcare supply chains are complex structures spanning across multiple organizational and geographical boundaries, providing critical backbone to services vital for everyday life. The inherent complexity of such systems can introduce impurities including inaccurate information, lack of transparency and limited data provenance. Counterfeit drugs is one consequence of such limitations within existing supply chains which not only has serious adverse impact on human health but also causes severe economic loss to the healthcare industry. Consequently, existing studies have emphasized the need for a robust, end-to-end track and trace system for pharmaceutical supply chains. Therein, an end-to-end product tracking system across the pharmaceutical supply chain is paramount to ensuring product safety and eliminating counterfeits. Most existing track and trace systems are centralized leading to data privacy, transparency and authenticity issues in healthcare supply chains. In this article, we present an Ethereum blockchain-based approach leveraging smart contracts and decentralized off-chain storage for efficient product traceability in the healthcare supply chain. The smart contract guarantees data provenance, eliminates the need for intermediaries and provides a secure, immutable history of transactions to all stakeholders. We present the system architecture and detailed algorithms that govern the working principles of our proposed solution. We perform testing and validation, and present cost and security analysis of the system to evaluate its effectiveness to enhance traceability within pharmaceutical supply chains.
Khaldoun Besoul, Ayman Nayef Al Halaybeh, Safwan Al Salaimeh
There are many changes related to the development of the Internet and e-business, which significantly impact the logistics sector's service sectors. Electronic logistics is not a new segment or a new standard in the classification process for logistics companies. It leads to the development and improvement of a wide range of logistics services. Blockchain technologies can be used to improve and develop services, which are an integral part of this evolution. This research sheds light on the mechanism and application of these technologies in logistics services and the extent of their impact on logistical activities where some technical aspects of Blockchain are studied in detail, including smart contracts that use the Ethereum platform as an example.