Rachit Agarwal, Tanmay Thapliyal, Sandeep K. Shukla
Smart Contracts (SCs) in Ethereum can automate tasks and provide different\nfunctionalities to a user. Such automation is enabled by the `Turing-complete'\nnature of the programming language (Solidity) in which SCs are written. This\nalso opens up different vulnerabilities and bugs in SCs that malicious actors\nexploit to carry out malicious or illegal activities on the cryptocurrency\nplatform. In this work, we study the correlation between malicious activities\nand the vulnerabilities present in SCs and find that some malicious activities\nare correlated with certain types of vulnerabilities. We then develop and study\nthe feasibility of a scoring mechanism that corresponds to the severity of the\nvulnerabilities present in SCs to determine if it is a relevant feature to\nidentify suspicious SCs. We analyze the utility of severity score towards\ndetection of suspicious SCs using unsupervised machine learning (ML) algorithms\nacross different temporal granularities and identify behavioral changes. In our\nexperiments with on-chain SCs, we were able to find a total of 1094 benign SCs\nacross different granularities which behave similar to malicious SCs, with the\ninclusion of the smart contract vulnerability scores in the feature set.\n
Adam Prasetya, Ferdiansyah Ferdiansyah, Yesi Novaria Kunang, Edi Surya Negara · 5 authors
Analisis sentiment saat ini banyak di gunakan masyarat sebagai bahan untuk mengetahui pendapat atau opini masyarakattentang berbagai macam hal. Dengan menggunakan sentiment analisis kita dapat mengklasifikasikan data apakah data tersebuttermasuk opini netral opini positif opini negatif. Penelitian ini membahas tentang analisis sentiment untuk mengukur tingkatakurasi dari pendapat masyarakat pada tiga cryptocurrency yaitu Bitcoin,ethereum,ripple dengan metode Naive Bayes dansupport vector machine yang berguna untuk mengetahui nilai akurasi yang tertinggi dari dua metode yang digunakan dalampenelitian ini. Ada banyak metode yang bisa digunakan untuk mengkasifikasikan opini tersebut, namun penelitian ini dipilihmetode Naive Bayes dan Support vector machine, dengan alasan metede tersebut banyak di gunakan oleh peneliti lain danmenghasilkan nilai akurasi yang tinggi. Hasil dari penelitian ini adalah berupa data perbandingan dari akurasi. hasil akurasidari 3 cryptocurrency SVM lebih besar dari pada nilai akurasi 3 cryptocurrency Naive Bayes.
This paper introduces a blockchain-based federated learning (FL) framework with incentives for participating nodes to enhance the accuracy of classification problems. Machine learning technology has been rapidly developed and changed from a global perspective for the past few years. The FL framework is based on the Ethereum blockchain and creates an autonomous ecosystem, where nodes compete to improve the accuracy of classification problems. With privacy being one of the biggest concerns, FL makes use of the blockchain-based approach to ensure privacy and security. Another important technology that underlies the FL framework is zero-knowledge proofs (ZKPs), which ensure that data uploaded to the network are accurate and private. Basically, ZKPs allow nodes to compete fairly by only submitting accurate models to the parameter server and get rewarded for that. We have conducted an analysis and found that ZKPs can help improve the accuracy of models submitted to the parameter server and facilitate the honest participation of all nodes in FL.
The study investigates the safe haven properties and sustainability of the top five cryptocurrencies (Bitcoin, Ethereum, Dash, Monero, and Ripple) and gold for BRICS stock markets during the COVID-19 crisis period from 31 January 2020 to 17 September 2020 in comparison to the precrisis period from 1 January 2016 to 30 January 2020, in a nonlinear and asymmetric framework using Nonlinear Autoregressive Distributed Lag (NARDL) methodology. Our results show that the relationship dynamics of stock market and cryptocurrency returns both in the short and long run are changing during the COVID-19 crisis period, which justifies our study using the nonlinear and asymmetric model. As far as a sustainable safe haven is concerned, Dash and Ripple are found to be a safe haven for all the five markets before the pandemic. However, all five cryptocurrencies are found to be a safe haven for three emerging markets, such as Brazil, China, and Russia, during the financial crisis. In a comparative framework, gold is found to be a suitable safe haven only for Brazil and Russia. The results have implications for index fund managers of BRICS markets to include Dash and Ripple in their portfolio as safe haven assets to protect its value during a stock market crisis.
Decentralized cryptocurrency exchanges offer compelling security benefits over centralized exchanges: users control their funds and avoid the risk of an exchange hack or malicious operator. However, because user assets are fully accessible by a secret key, decentralized exchanges pose significant internal security risks for trading firms and automated trading systems, where a compromised system can result in total loss of funds. Centralized exchanges mitigate this risk through API key based security policies that allow professional users to give individual traders or automated systems specific and customizable access rights such as trading or withdrawal limits. Such policies, however, are not compatible with decentralized exchanges, where all exchange operations require a signature generated by the owner's secret key. This paper introduces a protocol based upon multiparty computation that allows for the creation of API keys and security policies that can be applied to any existing decentralized exchange. Our protocol works with both ECDSA and EdDSA signature schemes and prioritizes efficient computation and communication. We have deployed this protocol on Nash exchange, as well as around several Ethereum-based automated market maker smart contracts, where it secures the trading accounts and wallets of thousands of users.
Fakultas Ekonomi dan Bisnis, Universitas Sumatera Utara, Medan, Septiana Sihombing, Muhammad Rizky Nasution, Fakultas Ekonomi dan Bisnis, Universitas Sumatera Utara, Medan · 6 authors
Purpose: This study aimed to analyze cryptocurrency fundamentals against price fluctuations (case study 2019-2020). Research Methodology: The data in the research paper were accessed on the coinmarketcap.com website. The sample of this research is Bitcoin, Ethereum, Litecoin, and Bitcoin Cash. Results: The results show that: 1) Bitcoin Market Cap, Ethereum Market Cap, Litecoin Market Cap, Bitcoin Cash Market Cap have a positive and significant effect on price fluctuations. 2) Bitcoin Volume and Bitcoin Cash Volume have a positive and insignificant effect on price fluctuations. 3) Ethereum volume has a positive and significant effect on price fluctuations. 4) Litecoin volume has a negative and significant effect on price fluctuations. Limitations: Further researchers are expected to add variables such as (global macroeconomics, cryptocurrency Blockchain technology changes, psychological factors), different variables (quantitative and qualitative) and combine models to predict cryptocurrency price fluctuations by analyzing them fundamentally and technically. Contribution: This research implies that cryptocurrencies with highly speculative prices have the same fundamental value as the stock price.
Piotr Pośpiech, Aleksander Mariański, Michał Kędziora
The paper presents decentralized voting scheme for verified users while maintaining their anonymity. A blockchain network was applied, which is a decentralized and distributed database based on the Peer-to-Peer architecture. During the implementation, the Ethereum network was used. Thanks to this, it is possible to code the terms of the contract required to perform the transaction. Ethereum and the use of smart contracts were also discussed in paper. The implementation uses the blind signature protocol by David Chaum and encryption with the Rivest-Shamir-Adleman (RSA) algorithm. Presented in this paper scheme for blockchain decentralized voting for verified users with focus on anonymity is then fully implemented and identified potential issues are analysed and discussed.
Стаття присвячена дослідженню проблематики створення та розвитку децентралізованої фінансової моделі та можливості її використання для кредитування на основі технології криптовалют. Метою статті є дослідження особливостей децентралізованої фінансової моделі для кредитування на основі технології Ethereum. Технологія децентралізованих програм для фінансування (DeFi), що дозволяють перетворювати традиційні фінансові послуги у послуги, які надаються без посередника за допомогою смарт-контрактів на блокчейні, поступово розширює власні можливості. Децентралізація усуває необхідність в довірених третіх сторонах, зменшуючи ринкові можливості посередників, отримані завдяки інформаційній перевазі, яку вони мають у порівнянні з учасниками угоди, використовуючи їх посередницькі послуги. На даний час більшість фінансових послуг DeFi складається з монетарно-банківських послуг; надання платформ однорангової позики; забезпечення вдосконалених фінансових інструментів, таких як децентралізовані біржі, ринки деривативів. Застосування технології блокчейн у фінансовому секторі привело до створення системи, здатної відтворювати стандартні фінансові послуги та пропонувати інноваційні рішення для розвитку цієї галузі. Система блокчейн реалізована Ethereum, саме тому вона є дуже ефективною для системи транзакцій, ніж інші транзакції, оскільки вона має технологічні особливості смарт-контракту, які пов’язані з минулою транзакцією. Пошук подальших варіантів використання децентралізованих фінансів, на основі можливостей блокчейну, має вирішальне значення для створення відкритих фінансових продуктів. У разі успіху система децентралізованих фінансів розширить можливості надання кредитних ресурсів на основі технології Ethereum з покращеними умовами.
In recent years, phishing scams have become the crime type with the largest money involved on Ethereum, the second-largest blockchain platform. Meanwhile, graph neural network (GNN) has shown promising performance in various node classification tasks. However, for Ethereum transaction data, which could be naturally abstracted to a real-world complex graph, the scarcity of labels and the huge volume of transaction data make it difficult to take advantage of GNN methods. Here in this paper, to address the two challenges, we propose a Self-supervised Incremental deep Graph learning model (SIEGE), for the phishing scam detection problem on Ethereum. In our model, two pretext tasks designed from spatial and temporal perspectives help us effectively learn useful node embedding from the huge amount of unlabelled transaction data. And the incremental paradigm allows us to efficiently handle large-scale transaction data and help the model maintain good performance when the data distribution is drastically changing. We collect transaction records about half a year from Ethereum and our extensive experiments show that our model consistently outperforms strong baselines in both transductive and inductive settings.
Sharding is a popular way to achieve scalability in blockchain protocols, increasing their throughput by partitioning the set of transaction validators into a number of smaller committees, splitting the workload. Existing approaches for blockchain sharding, however, do not scale well when concurrent transactions alter the same replicated state component—a common scenario in Ethereum-style smart contracts.
Since the emergence of Ethereum, blockchain-based decentralized applications (DApps) have become increasingly popular and important. To balance the security, performance, and costs, a DApp typically consists of two layers: an on-chain layer to execute transactions and store crucial data on the blockchain and an off-chain layer to interact with users. A DApp needs to synchronize its off-chain layer with the on-chain layer proactively. Otherwise, the inconsistent data in the off-chain layer could mislead users and cause undesirable consequences, e.g., loss of transaction fees. However, transactions sent to the blockchain are not guaranteed to be executed and could even be reversed after execution due to chain reorganization. Such non-determinism in the transaction execution is unique to blockchain. DApp developers may fail to perform the on-chain-off-chain synchronization accurately due to their lack of familiarity with the complex transaction lifecycle. In this work, we investigate the challenges of synchronizing on-chain and off-chain data in Ethereum-based DApps. We present two types of bugs that could result in inconsistencies between the on-chain and off-chain layers. To help detect such on-chain-off-chain synchronization bugs, we introduce a state transition model to guide the testing of DApps and propose two effective oracles to facilitate the automatic identification of bugs. We build the first testing framework, DArcher, to detect on-chain-off-chain synchronization bugs in DApps. We have evaluated DArcher on 11 popular real-world DApps. DArcher achieves high precision (99.3%), recall (87.6%), and accuracy (89.4%) in bug detection and significantly outperforms the baseline methods. It has found 15 real bugs in the 11 DApps. So far, six of the 15 bugs have been confirmed by the developers, and three have been fixed. These promising results demonstrate the usefulness of DArcher.
Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. In this paper, we explore combining deep learning with expert patterns in an explainable fashion. Specifically, we develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features. Thereafter, the global graph feature and local expert patterns are fused to cooperate and approach the final prediction, while yielding their interpretable weights. Experiments are conducted on all available smart contracts with source code in two platforms, Ethereum and VNT Chain. Empirically, our system significantly outperforms state-of-the-art methods. Our code is released.
This paper presents iBatch, a middleware system running on top of an operational Ethereum network to enable secure batching of smart-contract invocations against an untrusted relay server off-chain. iBatch does so at a low overhead by validating the server's batched invocations in smart contracts without additional states. The iBatch mechanism supports a variety of policies, ranging from conservative to aggressive batching, and can be configured adaptively to the current workloads. iBatch automatically rewrites smart contracts to integrate with legacy applications and support large-scale deployment. For cost evaluation, we develop a platform with fast and cost-accurate transaction replaying, build real transaction benchmarks on popular Ethereum applications, and build a functional prototype of iBatch on Ethereum. The evaluation results show that iBatch saves 14.6%-59.1% Gas cost per invocation with a moderate 2-minute delay and 19.06%-31.52% Ether cost per invocation with a delay of 0.26-1.66 blocks.
Flavio Corradini, Alessandro Marcelletti, Andrea Morichetta, Andrea Polini · 7 authors
As a disruptive technology, the blockchain is continuously finding novel application contexts, bringing new opportunities and radical changes. In this paper, we use blockchain as a communication infrastructure to support multi-party business processes. In particular, through smart contracts specifically generated by the mentioned business process, it is possible to derive a trustable infrastructure enabling the interaction among parties. Moreover, the emergence of different blockchain technologies, satisfying different characteristics, gives the possibility to support the same business process dealing with different non-functional needs. In this paper, we propose a novel engineering methodology supported by a practical framework called Multi-Chain. It permits to derive, using a model-driven strategy, a blockchain-based infrastructure, that can be deployed over a specific blockchain technology (e.g., Ethereum or Hyperledger Fabric). The objective is to permit the single definition and multiple deployments of the business process, to deliver the same functionalities, but satisfying different non-functional needs. In such a way, organisations willing to cooperate can select the multi-party business process and the blockchain technology they would like to use to satisfy their needs. Using Multi-Chain, they will be able to automatically derive from a Business Process Modelling Notation (BPMN) choreography diagram a blockchain infrastructure ready to be used. This overcomes the need to get acquainted with many details of the specific technology.
Blockchain-based smart contract platforms have traditionally employed the transaction-driven execution model. This paper presents an alternate framework for blockchain-based smart contract execution called EDSC. Our platform design presents a novel approach to tackle the scalability and performance challenges facing the smart contract ecosystem. We base EDSC's design on the Ethereum template, and it can be readily implemented for other existing smart contract platforms. To evaluate our design, we perform an experimental implementation using the Ethereum client. Our experiments with performance modeling show, on average, a 2.2 to 4.6 times reduced total latency of event-triggered smart contracts, demonstrating the effectiveness of the design in supporting time-sensitive applications. Additionally, we comment on the design's potential security aspects and demonstrate its utility by discussing potential use cases.
BACKGROUND: Health care systems are currently undergoing a digital transformation that has been primarily triggered by emerging technologies, such as artificial intelligence, the Internet of Things, 5G, blockchain, and the digital representation of patients using (mobile) sensor devices. One of the results of this transformation is the gradual virtualization of care. Irrespective of the care environment, trust between caregivers and patients is essential for achieving favorable health outcomes. Given the many breaches of information security and patient safety, today's health information system portfolios do not suffice as infrastructure for establishing and maintaining trust in virtual care environments. OBJECTIVE: This study aims to establish a theoretical foundation for a complex health care system intervention that aims to exploit a cryptographically secured infrastructure for establishing and maintaining trust in virtualized care environments and, based on this theoretical foundation, present a proof of concept that fulfills the necessary requirements. METHODS: This work applies the following framework for the design and evaluation of complex intervention research within health care: a review of the literature and expert consultation for technology forecasting. A proof of concept was developed by following the principles of design science and requirements engineering. RESULTS: This study determined and defined the crucial functional and nonfunctional requirements and principles for enhancing trust between caregivers and patients within a virtualized health care environment. The cornerstone of our architecture is an approach that uses blockchain technology. The proposed decentralized system offers an innovative governance structure for a novel trust model. The presented theoretical design principles are supported by a concrete implementation of an Ethereum-based platform called VerifyMed. CONCLUSIONS: A service for enhancing trust in a virtualized health care environment that is built on a public blockchain has a high fit for purpose in Healthcare 4.0.
Abdullah Lakhan, Mazin Abed Mohammed, Ahmed Noori Rashid, Seifedine Kadry · 7 authors
The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.
Alexandre Augusto Giron, Jean Everson Martina, Ricardo Felipe Custódio
Steganography is one of the ways to hide data between parties. Its use can be worrisome, e.g., to hide illegal communications. Researchers found that public blockchains can be an attractive place to hide communications; however, there is not much evidence of actual use in blockchains. Besides, previous work showed a lack of steganalysis methods for blockchains. In this context, we present a steganalysis approach for blockchains, evaluating it in Bitcoin and Ethereum, both popular cryptocurrencies. The main objective is to answer if one can find steganography in real case scenarios, focusing on LSB of addresses and nonces. Our sequential analysis included 253 GiB and 107 GiB of bitcoin and ethereum, respectively. We also analyzed up to 98 million bitcoin clusters. We found that bitcoin clusters could carry up to 360 KiB of hidden data if used for such a purpose. We have not found any concrete evidence of hidden data in the blockchains. The sequential analysis may not capture the perspective of the users of the blockchain network. In this case, we recommend clustering analysis, but it depends on the clustering method's accuracy. Steganalysis is an essential aspect of blockchain security.
Open access
Advanced Steganography and Watermarking Techniques
This article is devoted to the analysis of the legal framework for the digitization of public procurement, doctrinal approaches to the use of smart contracts in procurement. It studies the process and technologies of formation and development of smart contracts, blockchain, contracting in ethereum, the mechanisms of using smart contracts in public procurement. It also explores aspects that need to be addressed in improving the procurement system through smart contracts and develops recommendations.
Jonas Schiffl, Matthias Grundmann, Marc Leinweber, Oliver Stengele · 6 authors
Ethereum is a platform for deploying smart contracts, which due to their public nature and the financial value of the assets they manage are attractive targets for attacks. With asset management as a main task of smart contracts, access control aspects are naturally part of the application itself, but also of the functions implemented in a smart contract. Therefore, it is desirable to establish the correctness of smart contracts and their access control on application and single-function level through formal methods. However, there is no established methodology of formalising and verifying correctness properties of smart contracts. In this work, we make an attempt in this direction on the basis of a case study. We choose an existing smart contract application which aims to ascertain the integrity of binary files distributed over the Internet by means of decentralised identity management and access control. We formally specify and verify correctness at the level of single functions as well as temporal properties of the overall application. We demonstrate how to use verified low-level correctness properties for showing correctness at the higher level. In addition, we report on our experience with existing verification tools.
Kaihua Qin, Liyi Zhou, Pablo Gamito, Philipp Jovanovic · 5 authors
Financial speculators often seek to increase their potential gains with leverage. Debt is a popular form of leverage, and with over 39.88B USD of total value locked (TVL), the Decentralized Finance (DeFi) lending markets are thriving. Debts, however, entail the risks of liquidation, the process of selling the debt collateral at a discount to liquidators. Nevertheless, few quantitative insights are known about the existing liquidation mechanisms. In this paper, to the best of our knowledge, we are the first to study the breadth of the borrowing and lending markets of the Ethereum DeFi ecosystem. We focus on Aave, Compound, MakerDAO, and dYdX, which collectively represent over 85% of the lending market on Ethereum. Given extensive liquidation data measurements and insights, we systematize the prevalent liquidation mechanisms and are the first to provide a methodology to compare them objectively. We find that the existing liquidation designs well incentivize liquidators but sell excessive amounts of discounted collateral at the borrowers' expenses. We measure various risks that liquidation participants are exposed to and quantify the instabilities of existing lending protocols. Moreover, we propose an optimal strategy that allows liquidators to increase their liquidation profit, which may aggravate the loss of borrowers.
COVID-19 has made eHealth an imperative. The pandemic has been a true catalyst for remote eHealth solutions such as teleHealth. Telehealth facilitates care, diagnoses, and treatment remotely, making them more efficient, accessible, and economical. However, they have a centralized identity management system that restricts the interoperability of patient and healthcare provider identification. Thus, creating silos of users that are unable to authenticate themselves beyond their eHealth application's domain. Furthermore, the consumers of remote eHealth applications are forced to trust their service providers completely. They cannot check whether their eHealth service providers adhere to the regulations to ensure the security and privacy of their identity information. Therefore, we present a blockchain-based decentralized identity management system that allows patients and healthcare providers to identify and authenticate themselves transparently and securely across different eHealth domains. Patients and healthcare providers are uniquely identified by their health identifiers (healthIDs). The identity attributes are attested by a healthcare regulator, indexed on the blockchain, and stored by the identity owner. We implemented smart contracts on an Ethereum consortium blockchain to facilities identification and authentication procedures. We further analyze the performance using different metrics, including transaction gas cost, transaction per second, number of blocks lost, and block propagation time. Parameters including block-time, gas-limit, and sealers are adjusted to achieve the optimal performance of our consortium blockchain.