Blockchain is a digital technology built on three pillars: decentralization, transparency and immutability. Bitcoin and Ethereum are two prevalent Blockchain platforms, where the participants are globally connected in a peer-to-peer manner and anonymously perform trade electronically. The vast number of decentralized transactions and the pseudo-anonymity of participants open the door for scams, cyber frauds, hacks, money laundering and fraudulent transactions. It is challenging to detect such fraudulent activities using traditional auditing techniques, since they need more processing power, time and memory for complex queries to join combinations of tables. This paper proposes several algorithms to extract the transaction- related features from the Bitcoin and Ethereum networks and to represent the features as graphs. Moreover, the paper discusses how visualisation of graphs can reflect the anomalies and patterns of fraudulent activities.
In the era of the fourth industrial revolution, all aspects of the industrial domain are being affected by emerging technologies. Digitalization of every process is taking place or under process. One of the most important components common to every domain is the supply chain process. Organizations employ a digital supply chain to track the delivery of their products or materials. The digital supply chain is still suffering from a few issues such as no provenance, less transparency, and a trust issue. Blockchain technology, one of the emerging technologies, can be integrated with the supply chain to deal with the existing issues and to improve its performance. In this paper, a model is proposed to integrate blockchain technology with the supply chain to improve performance. The proposed model uses the combination of the Ethereum blockchain and the interplanetary file system to maintain the traceability, transparency, and trustworthiness of the supply chain.
Due to the transparency, simplicity, and blockchain system, cryptocurrencies gained popularity in the modern world. This led to more use of cryptocurrencies for speculation and investment rather than a medium of exchange. It is crucial to analyse the nature of the crypto market before investing in such currencies. With this intention, the paper tried to know the extent of following (Followness) of altcoins to the bitcoin in the different dominance phases like High Dominance, Low Dominance, and Moderate Dominance. For this purpose, daily closing prices of the Bitcoin and five major altcoins (Ethereum, Litecoin, Namecoin, Doge, and Ripple) are collected for the last five years and analyse the relationship between bitcoin and altcoins. Pearson's correlation coefficient test is used to know the direction of the relationship, and Vector Error Correction Model is used to see the extent of the relation. In general, the empirical result of the study showed cointegration between bitcoin and Altcoin. It also depicted that Altcoin showed a high level of followness in the moderate dominance phase and low followness in the low dominance phase. The study developed a price estimation equation to predict the price of altcoins depending upon the price of bitcoin and its dominance in the crypto market. This paper concludes that the dominance of Bitcoin also has a significant role in the price movement of altcoins.
This paper is a review of some real-time issues associated with the development of Ethereum smart contracts like out of gas exception and gas inefficient code patterns and focuses on the methods and solutions presented in recent years. With the help of this paper, we are trying to summarize the methods which can be used in future researches for gas prediction with the help of old transaction data and machine learning algorithms and have a look at old researches which are trying to predict with the help of regression algorithms and their efficiency. KEYWORDS: Blockchain, Etherium, Gas Consumption, Gas Prediction, Transaction
Public Key Infrastructure (PKI) is the fundamental of secure digital communications. It provides a secure means to authenticate identities over the Internet. Symmetric or asymmetric encryption schemes are widely used in identity authentication in any kind of PKI. The conventional PKI has several drawbacks due to the centralized and non-transparent design. Several recent research works utilize blockchain technology to overcome the limitations of conventional implementations of PKI. Blockchain-based PKI integrates blockchain technology with PKI to form a new type of decentralized PKI (DPKI). Several works utilize the currency property in blockchains to implement the reward-and-punishment mechanism. In this paper, we propose a smart contract-based PKI which utilizes the Ethereum smart contract to build a new type of blockchain-based PKI with the reward-and-punishment mechanism using ERC-20 tokens. It has several advantages over previous implementations of similar research that use Ethereum’s main currency—Ether.
Smart contracts, scripts at the heart of blockchain-based applications, are meant to be available forever once deployed. However, this property has a price. The amount of space required to store new contracts keeps increasing. This increase impacts each participating node's performance and makes it inconvenient for low-end devices to participate in the network. Among all contracts deployed in the blockchain, a vast majority will lead to little if any usage. We demonstrate that, in the course of one year, 70 % of deployed contracts lead to no use. Unfortunately, unused contracts keep occupying space on the blockchain. To tackle this issue, we propose a new protocol to identify and delete unused contracts. Through simulation, based on Ethereum historical data, we show that deletion of smart contracts after an inactivity period of 90 days could lead to a 66 % reduction in the number of contracts stored over a year.
The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes.
This paper initially presents a nontechnical overview of cryptocurrency, its history, and the technicalities of its usage as a means of exchange. Bitcoin’s working methodology and mathematical baseline is further presented in more depth. For the remaining majority of the paper, recent cryptocurrency price data of Bitcoin, Ethereum, Tether, Dogecoin, and Binance coin was used to train a machine learning model of Feed Forward Neural Networks to predict future prices for each of the datasets. Further and in conclusion, the results are discussed, and the efficiency and accuracy of these models are evaluated.
Hanlin Zhang, Peng Gao, Jia Yu, Jie Lin · 5 authors
Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work, and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.
Blockchain is a reliable and innovative technology that harnesses education and training through digital technologies. Nonetheless, it has been still an issue keeping track of student/graduate academic achievement and blockchain access rights management. Detailed information about academic performance within a certain period (semester) is not present in the official education documents. Furthermore, academic achievement documents issued by institutions are not secured against unauthorized changes due to the involvement of intermediaries. Therefore, verification of official educational documents has become a pressing issue owing to the recent development of digital technologies. However, effective tools to accelerate the verification are rare as the process takes time. This study provides a prototype of the UniverCert platform based on a consortium version of the decentralized, open-source Ethereum blockchain technology. The proposed platform is based on a globally distributed peer-to-peer network that allows educational institutions to partner with the blockchain network, track student data, verify academic performance, and share documents with other stakeholders. The UniverCert platform was developed on a consortium blockchain architecture to address the problems universities face in storing and securing student data. The system provides a solution to facilitate students’ registration, verification, and authenticity of educational documents.
Fahad Mostafa, Pritam Saha, Mohammad Rafiqul Islam, Nguyet Nguyen
Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Then, we use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the GJR-GARCH model, and calculate the value at risk (VaR) of the simulations. We also estimate the tail-risk using VaR backtesting. Finally, we use an artificial neural network (ANN) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors (MSEs) from the ANN models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the ANN models perform better than traditional ARIMA models.
Abstract Next to cryptocurrencies, tokens are a widespread application area of blockchains. Tokens are digital assets implemented as small programs on a blockchain. Being programmable makes them versatile and an innovative means for various purposes. Tokens can be used as investment, as a local currency in a decentralized application, or as a tool for building an ecosystem or a community. A high-level categorization of tokens differentiates between payment, security, and utility tokens. In most jurisdictions, security tokens are regulated, and hence, the distinction is of relevance. In this work, we discuss the identification of tokens on Ethereum, the most widely used token platform. The programs on Ethereum are called smart contracts, which—for the sake of interoperability—may provide standardized interfaces. In our approach, we evaluate the publicly available transaction data by first reconstructing interfaces in the low-level code of the smart contracts. Then, we not only check the compliance of a smart contract with an established interface standard for tokens, but also aim at identifying tokens that are not fully compliant. Thus, we discuss various heuristics for token identification in combination with possible definitions of a token. More specifically, we propose indicators for tokens and evaluate them on a large set of token and non-token contracts. Finally, we present first steps toward an automated classification of tokens regarding their purpose.
Blockchain is not widely applied in various fields due to the critical issue of scalability as part of the blockchain trilemma. This issue arises during consensus among the nodes in a public blockchain. To address the issue of low scalability with proof-of-work (PoW) consensus, various methods have been proposed for transaction per second (TPS) improvement. However, no such methods include an improvement in the consensus step. Therefore, to improve PoW public blockchain scalability, it is important to shorten the time required for PoW consensus. This paper proposes a method for minimizing the block intervals that occur during consensus over a PoW blockchain network. A shortened block interval leads to an increase in the probability of three different attacks: selfish mining, double-spending, and eclipse attacks. According to an experiment using Ethereum, with a typical PoW blockchain, it is inevitable to provide rewards for stable block mining in competition between mining pools. To find an optimal block interval in the PoW consensus algorithm, we conducted a four-step experiment. The purpose of this experiment was to verify the difficulty level and issues with Mainnet security. Therefore, considering stale block mining rewards, an optimal block interval is proposed. The Ethereum TPS was improved by at least 200%. Given this finding, it is considered possible to achieve a similar improvement in a different PoW blockchain. On balance, even if the block interval is shorter than that of the PoW Mainnet, network security falls by only 1.21% in Testnet, even with a rise in the stale block rate, while performance is increased at up to 120 TPS, which is three times higher than that in Mainnet.
Objetivo: Organizar en forma experimental carteras de inversión con los criptoactivos bajo la teoría moderna de estructuración de portafolios de inversión de Markowitz. Método: La investigación fue del tipo cuantitativa y experimental, abarcando el proceso de estructuración de portafolios de inversión con criptomonedas bajo la teoría de Markowitz, mediante el modelo de programación no lineal cuantitativa. Resultados: Se pudo observar que el periodo de cotización más pronunciado se presentó en el periodo comprendido entre 2017 a 2018. Una rentabilidad mayor le corresponde a Ethereum, seguido de Bitcoin y de Ripple; consecuentemente, Ethereum es el que tiene el mayor indicador de volatilidad. La opción de cartera eficiente se obtuvo cuando se invirtió el 70% en Bitcoin, 14% en Ethereum, 6% en Ripple y 10% en Thether y, como efecto de la diversificación de carteras de inversión, se verifica el comportamiento inverso de la volatilidad. Conclusiones: En el estudio en referencia, se demostró el efecto del número de activos en la conformación del portafolio de inversiones en la reducción de la volatilidad y las opciones que provee el mapa de la frontera eficiente para que los inversionistas opten por las opciones que calcen mejor con sus expectativas de riesgo y rentabilidad.
Abstract Technological developments have always led to changes in all aspects of our lives. Crypto currency is one of those changes. As a result of those changes, thousands of currencies such as bitcoin, ripple, litecoin and ethereum have evolved and have found a use in business. The present study focuses upon Ripple and tries to explain its effects on banks and business theoretically. It has been stated that the money transfer performed through Ripple is faster and more economical when compared to present systems. Additionally, it has been realised that the present SWIFT system has been influenced by that speed and economy, and therefore taken considerable technologic steps with an effort to improve its system.
Cryptocurrency has appeared recently but become important part of the global financial sector. Some experts predict that cryptocurrency has changed the world forever because of its utility to transfer payments across borders with little cost or delay. Cryptocurrencies are traded with no broker and tracked on digital ledgers. Dramatic changes in the value of the cryptocurrency attract the investors' attention. Some of the have a one-day trading strategy, other investors construct crypto portfolio for a long time periods. The object of this study is value of Bitcoin, Ethereum, and Litecoin on short time periods and long time periods. The subject of the study is the statistical analysis methods to assess the different cryptocurrencies investment efficiency on the different time periods. The aim of the study is to find out what cryptocurrency strategy is better to use for investors. The analysis is carried out on randomly generated periods for 8 samples cryptocurrencies value. The result of the study allows to state that investor does not need to construct crypto portfolio based on its profitability. An effective strategy is to invest money for long time periods in any cryptocurrencies whose utilities meet the investor's requirements.
Traditional data collection, storage and processing of Electronic Health Records (EHR) utilize centralized techniques that pose several risks of single point of failure and lean the systems to a number of internal and external data breaches that compromise their reliability and availability. Blockchain is an emerging distributed technology that can solve these issues due to its immutability and architectural nature that prevent records manipulation or alterations. In this paper, we discuss the progress and opportunities of remote patient monitoring using futuristic blockchain technologies and its two primary frameworks: Ethereum and Hyperledger Fabric. We also discuss the possible blockchain use cases in software engineering for systematic, disciplined, and quantifiable application development. The study extends by introducing a system architecture for EHR data management using Ethereum as a model. We discuss the challenges and limitations along with the initial evaluation results of the proposed system and draw future research directions in this promising area.
Many implementation of blockchain technology are widely available today. This Project explains how blockchain technology improves efficiency and builds faith in funding process of startup process which affect today’s business and industries. Designing Block chain based a decentralized, distributed ledger, which records transactions or events of funding process in Start-up is discussed.Start –ups facing an issue of raising a required fund. Although there are many sources are available to entrepreneurs who wish to begin new businesses or expand existing ones, like family, friends, friends of friends, bank loan, use of internet, online crowd funding platform and many more. However, to look at the proper distribution and utilization of money and to keep track of it is main problem. This proposed system may provide the solution with blockchain technology for issues related to crowdfunding contract. Through the notion of smart contract automated interaction between and existing transaction system is discussed. A solution for the issues like security, abuse of investor and illegal transactions in crowdfunding process is prompted in this project.The idea behind model is to use ethereum based smart contract for securely and effectively handling connection between fundraisers,vendors and project manager/idea person. Blockchain enabled,distributed platforms is used to avoid fraud and to view proper utilization and distribution of money raised by different contributors.
With the rapid development of e-commerce services, online retail has evolved from multi-channel to omni-channel in order to provide customers with more services. However, reverse logistics services (returns and exchanges) have become the target of many fraudulent activities, causing a lot of economic losses for many online retail companies. The current challenge of the traditional countermeasure is it requires a lot of manpower and training resources. In this study, we propose ESPRES, a system that adopts blockchain technology to prevent fraudulent behavior in the process of returns and exchanges with the smart contract and multi-attribute decision-support method to help consumers choose a suitable payment program. A practical implication of this study is that by adopting blockchain technology, a great amount of manpower used on determining whether each return or exchange is fraudulent can be reduced since merchants can check the product ownership. In addition, due to the fact that the footprint of goods cannot be forged, it can also prevent counterfeit or parallel imports of goods.
Hardware wallets are designed to withstand malware attacks by isolating their private keys from the cyberspace, but they are vulnerable to the attacks that fake an address stored in a clipboard. To prevent such attacks, a hardware wallet asks the user to verify the recipient address shown on the wallet display. Since crypto addresses are long sequences of random symbols, their manual verification becomes a difficult task. Consequently, many users of hardware wallets elect to verify only a few symbols in the address, and this can be exploited by an attacker. In this work, we introduce EthClipper, an attack that targets owners of hardware wallets on the Ethereum platform. EthClipper malware queries a distributed database of pre-mined accounts in order to select the address with maximum visual similarity to the original one. We design and implement a EthClipper malware, which we test on Trezor, Ledger, and KeepKey wallets. To deliver computation and storage resources for the attack, we implement a distributed service, ClipperCloud, and test it on different deployment environments. Our evaluation shows that with off-the-shelf PCs and NAS storage, an attacker would be able to mine a database capable of matching 25% of the digits in an address to achieve a 50% chance of finding a fitting fake address. For responsible disclosure, we have contacted the manufactures of the hardware wallets used in the attack evaluation, and they all confirm the danger of EthClipper.
Elections are a democratic activity in Indonesia which are carried out every five years to elect leaders, but in the process of manual election calculations, the results are leaked and can cause data security problems. Through this research, we can provide solutions by implementing blockchain technology in election applications that will provide security for election results data stored on the blockchain. Blockchain technology is a chain of data blocks that are connected to each other by peer to peer. This application uses the Solidity programming language and uses a local ethereum blockchain database (Ganache). The method used in making this application is the waterfall method with Unified Modeling Language (UML) modeling where the visual modeling method is in object-oriented system design. From the results of testing the blockchain system for this application, it is found that blockchain can help secure election results very securely, where every result data will be stored in every block in a decentralized blockchain network.
Smart homes, connected through a network, can optimize the energy consumption and general load shape of their area. In this work, a blockchain-based smart solution is presented for demand-side management of residential buildings in a neighborhood to improve Peaks to Average Ratios (PAR) of power load, reduce energy consumption, and increase the thermal comfort of occupants by modeling heating, illumination, and appliance systems. For real-time power and temperature monitoring of the neighborhood, a transient numerical physical model has been developed. The simulator has been validated with data measured from a building in Northern Italy. Then, a neighborhood with 2,000 households has been modeled for different occupancy patterns, initial values, and boundary conditions. Two different control scenarios, namely basic and smart, have been considered. In the basic scenario, everything is managed by occupants except the boiler, which is controlled by the indoor temperature of the home. Instead, in the smart scenario, a blockchain-based network has been introduced for buildings to exchange a parameter called the Probability of the Next Hour (PNH). Ethereum Solidity has been deployed for smart contract development in the blockchain. The results show that using blockchain-connected smart controllers aimed at demand-side management can improve PAR, comfort level, and energy efficiency of buildings, which can bring about CO2 reduction on an urban and even global scale.
The growing web of data warrants better data management strategies. Data silos are single points of failure and they face availability problems which lead to broken links. Furthermore the dynamic nature of some datasets increases the need for a versioning scheme. In this work, we propose a novel architecture for a linked open data infrastructure, built on open decentralized technologies. IPFS is used for storage and retrieval of data, and the public Ethereum blockchain is used for naming, versioning and storing metadata of datasets. We furthermore exploit two mechanisms for maintaining a collection of relevant, high-quality datasets in a distributed manner in which participants are incentivized. The platform is shown to have a low barrier to entry and censorship-resistance. It benefits from the fault-tolerance of its underlying technologies. Furthermore, we validate the approach by implementing our solution.
Smart grid is an innovation in a communication network, interconnected power framework, advanced control technology, and smart metering has been applied to work on the usage of renewable energy resources and alleviate the energy emergency somehow. Blockchain is basically a decentralized accounting ledger with the potential to enable, manage, track and verify thousands of energy transactions per second. The blockchain energy market is proposed to be utilized by networks that share a nanogrid. The application permits nanogrid participants that have an abundance of electrical energy, to offer that energy to different clients of the nanogrid. The application gives a decentralized marketplace for executing electrical energy. Since this blockchain energy market is implemented by Ethereum smart contract, it suffers from the high operating costs that result from the contract's high gas consumption. Ethereum smart contracts are implemented by reworking on acceptoffer function to verifies its validity and withdraw function which uses a single function call for multiple energy transfer contracts to decrease the operation cost.