Aiming at the transaction problem of multi-source heterogeneous distribution network, a P2P intelligent contract based on blockchain technology is proposed. Firstly, a multi-source heterogeneous distribution network model based on Intelligent contract is established. Secondly, a multi-source heterogeneous distribution network market transaction mechanism is proposed. The mechanism designs different kinds of transaction members, frameworks and processes. Transaction members can participate in the market quotation and determine the transaction unit price according to their own power surplus and shortage state. Finally, a smart contract solution with blind beat mechanism is designed for the proposed transaction mechanism. The effectiveness of the transaction mechanism is verified by an example and deployed on the Ethereum private chain. The effectiveness of the transaction mechanism is verified by an example and deployed on the Ethereum private chain. Through the simulation transaction analysis, it can be seen that the above transaction mechanism effectively improves the economic benefits of trading members and effectively ensures the market security and free competition.
In recent years, digital innovations especially emerged depend on Blockchain technology have caused a substantial transformation in the finance sector as in other sectors. Different financial assets have been revealed and began to be used as an investment tool along with this transformation in the markets. Cryptocurrencies that have a digital structure hold an important place among these assets. Dramatically increases in the daily transaction volume of currencies in the market have brought along different types of risks. These risks raised uncertainty on these currencies. Moreover, because cryptocurrencies are mostly used for the purpose of investment and speculation, it is important to understand the volatility movements and co-movements of cryptocurrencies and is substantially important, particularly because volatility can influence investment decisions. This study aims to determine the volatility transmission between cryptocurrencies to find useful answers about the volatility and the efficiency of markets. Daily logarithmic return series between 18 January 2018 â 14 February 2021 were used to analyze the volatility of five of the most common cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), IOTA by applying the RALS-ADF test, EGARCH, and DCC-GARCH models. We determined whether the market is efficient or not, and tested the existence of the asymmetric effect and volatility transmission in the market. According to our results, volatility shocks are not obtained persistent for only BTC. Furthermore, the presence of asymmetric effects and leverage effect valid for four cryptocurrencies. While asymmetric effects observed for BTC, no leverage effect has been observed during the period. We also analyzed nine pair-wise cryptocurrencies applying the DCC-GARCH model and we found that dynamic conditional correlation coefficients are statistically significant and positive for each pair.
Digital voting is increasingly important in both established and emerging democracies. Some of the advantages of digital voting are faster vote count and tabulation; accurate results; increased votersâ participation and convenience; and effective handling of complex electoral system formats that require laborious counting procedures. However, transparency, credibility, and integrity concerns, as well as the limited possibility of recount, usually make traditional digital voting systems unpopular. Digital voting using blockchain technology, however, is safe, transparent, and immutable, which makes it a suitable choice for future decentralized voting systems. In particular, the Ethereum blockchain is proposed as an appropriate platform for the backbone of an e-voting system due to its widespread use, transparency, consistency and provision of smart contracts. Initial piloting on the implementation of a blockchain-based voting framework in Jordan shows promising results on its transparency and integrity by incorporating a space for representatives and observers to monitor the election procedure and results as an additional measure to ensure its efficiency and reliability. The uptake of the proposed system calls for further debate and dialogue amongst governments and people, especially in developing countries where democracy is still in its infancy.
Compact block, which replaces transactions in the block with their hashes, is an effective means to speed up block propagation in the Bitcoin network. The compact block mechanism in Bitcoin counts on the fact that many nodes may already have the transactions (or most of the transactions) in the block, therefore sending the complete block containing the full transactions is unnecessary. This fact, however, does not hold in the Ethereum network. Adopting compact block directly in Ethereum may degrade the block propagation speed significantly because the probability of a node not having a transaction in the sending block is relatively high in Ethereum and requesting the missing transactions after receiving the compact block takes much additional time. To investigate the factors that prevent compact block in Ethereum, we set up probe nodes to collect data from Ethereum MainNet and performed data analysis. Our analysis results indicate that the missing transactions could be attributed to factors such as small transaction pools, network latency, and miners' selfish behaviors. Moreover, simply enlarging the transaction pool and using the prediction algorithm proposed for Bitcoin to predict the missing transactions and prefetch them do not work for Ethereum. This article proposes hybrid-compact block (HCB), an efficient compact block propagation scheme for Ethereum and other similar blockchains. First, we develop a Secondary Pool to store the low-fee transactions, which are removed from the primary transaction pool, to conserve storage space. As simple auxiliary storage, the Secondary Pool does not affect the normal block processing of the primary pool in Ethereum. Second, we design a machine learning-based transaction prediction module to precisely predict the missing transactions caused by network latency and selfish behaviors. We implemented our HCB scheme and other compact-block-like schemes (as benchmarks) and deployed a number of worldwide nodes over Ethereum MainNet to experimentally investigate them. Experimental results show that HCB performs best among the existing compact-block-like schemes and can reduce propagation time by more than half with respect to the current block propagation scheme in Ethereum.
As more and more data is produced, finding a secure and efficient data access structure has become a major research issue. The centralized systems used by medical institutions for the management and transfer of Electronic Medical Records (EMRs) can be vulnerable to security and privacy threats, often lack interoperability, and give patients limited or no access to their own EMRs. In this paper, we first propose a privilege-based data access structure and incorporates it into an attribute-based encryption mechanism to handle the management and sharing of big data sets. Our proposed privilege-based data access structure makes managing healthcare records using mobile healthcare devices efficient and feasible for large numbers of users. We then propose a novel distributed multilevel EMR (d-EMR) management scheme, which uses blockchain to address security concerns and enables selective sharing of medical records among staff members that belong to different levels of a hierarchical institution. We deploy smart contracts on Ethereum blockchain and utilize a distributed storage system to alleviate the dependence on the record-generating institutions to manage and share patient records. To preserve privacy of patient records, our smart contract is designed to allow patients to verify attributes prior to granting access rights. We provide extensive security, privacy, and evaluation analyses to show that our proposed scheme is both efficient and practical.
Abstract: Predicting future events is difficult, particularly with regards to cryptocurrency, where the media, influential people and governments have a sharp and vital impact on worth. Cryptocurrency market analysis is a method through which the realworld data of the cryptocurrency market is used to predict where it will go next. If foretold accurately, it helps investors to invest when the value is low (purchasing in bulk when the price is dipping) and sell once it's high so as to gain a profit. This research provides two machine learning algorithms which are Long Short-Term Memory (LSTM) and Linear Regression for predicting the values of six different types of crypto currencies such as Bitcoin (BTC), Dash coin (DASH), Lite coin (LTC), Dogecoin (DOGE), Ethereum (ETH), and Monero (XMR). The accuracy of the models is analyzed using mean squared error.
William Hughes, Tobias Magnusson, Alejandro Russo, Gerardo Schneider
Smart contracts are self-executing programs running in the blockchain allowing for decentralised storage and execution without a middleman. On-chain execution is expensive, with miners charging fees for distributed execution according to a cost model defined in the protocol. In particular, transactions have a high fixed cost. We present MultiCall, a transaction-batching interpreter for Ethereum that reduces the cost of smart contract executions by gathering multiple usersâ transactions into a batch. Our current implementation of MultiCall includes the following features: the ability to emulate Ethereum calls and create transactions, both from MultiCall itself and using an identity unique to the user; the ability to cheaply pay Ether to other MultiCall users; and the ability to authorise emulated transactions on behalf of multiple users in a single transaction using hash-based authorisation rather than more expensive signatures. This improves upon a previous version of MultiCall. Our experiments show that MultiCall provides a saving between 57% and 99% of the fixed transaction cost compared with the standard approach of sending Ethereum transactions directly. Besides, we also show how to prevent an economic attack exploiting the metatransaction feature, describe a generic protocol for hash-based authorisation of metatransactions, and analyse how to minimise its off-chain computational and storage cost.
Social distancing has been imposed to prevent substantial transmission of the COVID-19 outbreak, which is presently a global public health issue. Medical healthcare providers rely on telemedicine to monitor their patients, particularly those with chronic conditions. However, telemedicine faces many implementation-related risks, including data breaches, access restrictions within the medical community, inaccurate diagnosis, fraud, etc. The authors propose a transparent, tamper-proof, distributed, decentralized smart healthcare system (DSHS) that uses blockchain-based smart contracts. The authors use an immutable modified Merkel tree structure to hold the transaction for viewing contracts on a public blockchain, updating patient health records (PHR), and exchanging PHR to all entities. It is verified by a performance evaluation based on the Ethereum platform. The simulation results show that the proposed system outperforms existing approaches by enhancing transparency, boosting efficiency, and reducing average latency in the system. The proposed system improves the functionality of the SHS environment.
Mathematical ranking plays a critical role in the era of the internet and bigdata. Google's PageRank is well-known as a trillion-dollar algorithm. Definitely, algorithmic ranking frameworks are found on every search engine. In this paper, the article shall investigate how PageRank can be applied in the blockchain space to build up reliable and verifiable social credit and reputation systems. It is expected to provide a measure of credibility complementary and parallel with FICO, which is not applicable for individuals lacking credit information in financial institutions. Moreover, the approach proposes an unbiased method of interpreting and measuring real social interaction and reputation ranking on a blockchain network. The authors envision a future of payment based on cryptocurrencies (especially stable coins) and digital fiats; thus the proposed credit scoring framework shall be helpful for P2P credit and lending networks, possibly for decentralized finance (Defi) applications.
Front-running attacks have been a major concern on the blockchain. Attackers launch front-running attacks by inserting additional transactions before upcoming victim transactions to manipulate victim transaction executions and make profits. Recent studies have shown that front-running attacks are prevalent on the Ethereum blockchain and have caused millions of US dollars loss. It is the vulnerabilities in smart contracts, which are blockchain programs invoked by transactions, that enable the front-running attack opportunities. Although techniques to detect front-running vulnerabilities have been proposed, their performance on real-world vulnerable contracts is unclear. There is no large-scale benchmark based on real attacks to evaluate their capabilities. We make four contributions in this paper. First, we design an effective algorithm to mine real-world attacks in the blockchain history. The evaluation shows that our mining algorithm is more effective and comprehensive, achieving higher recall in finding real attacks than the previous study. Second, we propose an automated and scalable vulnerability localization approach to localize code snippets in smart contracts that enable front-running attacks. The evaluation also shows that our localization approaches are effective in achieving higher precision in pinpointing vulnerabilities compared to the baseline technique. Third, we build a benchmark consisting of 513 real-world attacks with vulnerable code labeled in 235 distinct smart contracts, which is useful to help understand the nature of front-running attacks, vulnerabilities in smart contracts, and evaluate vulnerability detection techniques. Last but not least, we conduct an empirical evaluation of seven state-of-the-art vulnerability detection techniques on our benchmark. The evaluation experiment reveals the inadequacy of existing techniques in detecting front-running vulnerabilities, with a low recall of$\leq$6.04%. Our further analysis identifies four common limitations in existing techniques: lack of support for inter-contract analysis, inefficient constraint solving for cryptographic operations, improper vulnerability patterns, and lack of token support.
The research paper aimed to investigate the relationship between the major popular cryptocurrencies in terms of market dominance and identify any pattern and/or causality between the short-run and long-run series. Cryptocurrency has received much attention because of media publicity and the financial returns it generates within a short time, with its associated risk level. This innovative financial research investigates for the first time by thoroughly analyzing nine top cryptocurrencies, excluding stablecoins. The study used the Vector Error Correction model to analyse how the various cryptocurrency under investigation are interconnected. The results demonstrated how concentrated the causality effect is on some specific cryptocurrencies. The study uses the top nine cryptocurrencies on the crypto markets, excluding stablecoins that have existed since October 2017. The frequency of the data is 1523 daily closing prices. The choice of the data stemmed from its availability and has existed since October 2017. The primary outcome is clear and possibly explains the dominance of Bitcoin and Ethereum as the main drivers of the prices of related or altcoins. Any movement in the price level of the two dominant cryptos affects all the altcoins on the crypto market. The research further unearths the interconnection or correlation between the major cryptocurrencies. It will assist institutional and retail investors, fund managers, and managers, with the possible mix of assets in their portfolio based on their risk appetite level in making investment decisions.
The scalability problem has been one of the most significant barriers limiting the adoption of blockchains. Blockchain sharding is a promising approach to this problem. However, the sharding mechanism introduces a significant number of cross-shard transactions, which are expensive to process. This paper focuses on the transaction allocation problem to reduce the number of cross-shard transactions for better scalability. In particular, we systematically formulate the transaction allocation problem and convert it to the community detection problem on a graph. A deterministic and fast allocation scheme TxAllo is proposed to dynamically infer the allocation of accounts and their associated transactions. It directly optimizes the system throughput, considering both the number of cross-shard transactions and the workload balance among shards. We evaluate the performance of TxAllo on an Ethereum dataset containing over 91 million transactions. Our evaluation results show that for a blockchain with 60 shards, TxAllo reduces the cross-shard transaction ratio from 98% (by using traditional hash-based allocation) to about 12%. In the meantime, the workload balance is well maintained. Compared with other methods, the execution time of TxAllo is almost negligible. For example, when updating the allocation every hour, the execution of TxAllo only takes 0.5 seconds on average, whereas other concurrent works, such as BrokerChain (INFOCOM'22) leveraging the classic METIS method, require 422 seconds.
Blockchain is a new and modern technology that is gradually being used in various fields due to its ability to decentralize and organize secure and reliable data exchange and storage. One of the related research areas generating increasing interest is the field of education, with particular focus on the digitization and automation of educational management processes and the ability to store and verify digital documents about student progress. The main goal of this study is to develop a platform that creates a unified digital register of studentsâ educational achievements, which is one of the most pressing issues in the field of education, based on the Ethereum blockchain architecture. Blockchain is expensive; therefore, there is a need to consider performance criteria when evaluating any decision made about the technology, especially the most important aspects such as predicting traffic behavior, estimating transaction costs and providing the necessary indicators of system quality and functionality. However, most research ignores the evaluation of performance indicators, such as throughput, the speed of transactions and the amount of data stored in the Ethereum blockchain database, which are the main evaluation criteria. This paper aims to eliminate this gap by evaluating the performance of the developed platform and by discussing the obtained experimental results. Thus, the main results of this work are the design and deployment of a blockchain platform and the analysis of its transaction costs. We conclude that the proposed blockchain solution is applicable as a system for the accounting and verification of loans and student academic achievements.
Automated Market Makers (AMMs) are a class of smart contracts on Ethereum and other blockchains that "make markets" autonomously. In other words, AMMs stand ready to trade with other market participants that interact with them, at the conditions determined by the AMM. In this this paper, which relies on the existing and growing corpus of literature available, we review and present the key mathematical and quantitative finance aspects that underpin their operations, including the interesting relationship between AMMs and derivatives pricing and hedging.
Ayodeji Ibitoye, Halleluyah Oluwatobi Aworinde, Esther T. Adekunle
Originally, manual voting systems are surrounded with issues like results manipulation, errors and long result computation time, ineligible voters, void votes among others. Electronic voting system helped in overcoming the challenges with manual voting system, to engendered other problems of phishing, men in the middle attack alongside voterâs impersonation. By these challenges, the integrity of an election results in a distributed system has become another top concern for e-voting system based on reliability. To achieve an improved votersâ authentication and result validation with excellent user experience, here, a Facial Recognition Electronic Voting System that is power-driven by Blockchain Technology was developed. The entire election engineering activities are decentralised with improved security features to enhance transparency, verifiability, and accountability for each vote count. The self-service voting system was built by smart contract and implemented on the Ethereum network. The obtained reports and evaluations reflected a non-editable and self-sufficiently certifiable system for voting. It also has a competitive edge over fingerprint enabled e-voting system. Aside itâs excellent usability and general acceptance, the developed method discarded to a larger extend, intended fraudulent actions from election activities by eliminating the involvement of a middleman while facilitating privacy, convenience, eligibility and satisfactory votersâ right.
Open access
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Taufiq Gilang Adhitama, Anggunmeka Luhur Prasasti, Ali Fahmi Perwira Negara
Baitul Maal Wa At Tamwil (BMT) is a sharia financial institution that provides savings and loan services in accordance with the social, cultural, and economic needs of rural communities, especially in agricultural and plantation communities. The current data management is still using manual recording and a centralized server which can cause fraudulent financial reports and creates a lack of credibility between BMT and its customers. The research method is to decentralize the application data system by using blockchain technology, then replacing the conventional database to blockchain system. The simulation shows that the e-BMT application are connected to blockchain network as intended, users can use metamask to interact with the Ethereum network, the blockchain implementation on e-BMT application has run according to expectations with a 100% success rate with the average transfer time on two devices of 9.47 seconds and 12.13 seconds. While the results of data entry time on two devices obtained an average of 9.96 seconds and 37.09 seconds. While the blockchain implementation on e-BMT could provide access to every user so that each entity could confirm the validity of the transactions, the size of the transactions, and other data recorded on the blockchain without having to develop an integrated database system. The research contributes in two aspects, first, we develop the distributed blockchain system using public Ethereum blockchain network integrated with with popular e-wallet such as metamask, provides easy access for both customers and BMT parties who are connected to the network so that the recorded data can be accessed by anyone, and second, the application of blockchain technology to BMT is capable to interact with users as it is built on a website platform with RESTful API.
Blockchain-based supply chains provide a new solution to decentralized multi-party product management. However, existing methods, including ID-based and cryptographic-based solutions, cannot achieve both counterfeit resistance and decentralization in supply chain management. We argue that this dilemma comes from the disconnection and inconsistency of the data records and physical product entities. This paper proposes RF-Chain, a novel decentralized supply chain management solution that uniquely combines data record authentication and physical-layer RFID tag authentication to effectively achieve credibility and counterfeit resistance. The main contribution of this work is to integrate physical-layer authentication of cheap commodity RFID tags into a blockchain-based information management system, and RF-Chain is the first to do so. The proposed cross-layer authentication can effectively defend against counterfeit attacks without relying on a central key management service. Real-world experiments utilizing the Ethereum (ETH) platform and more than 100 RFID tags demonstrate that RF-Chain is secure, effective, time-efficient, and cost-efficient.
Paolo Bottoni, Claudio Di Ciccio, Remo Pareschi, Domenico Tortola ¡ 6 authors
Smart contracts show a high potential to make supply chain management strategies epochally leap towards higher levels of productivity, not only in the functioning of production processes but also in terms of product innovation and overall economic returns. This article illustrates the principle of Income Sharing as a highly performing economic strategy for supply chains with a natural implementation in blockchain smart contracts. It proposes a blockchain-based architecture that uses smart contracts to implement various algorithmic versions of the Income Sharing principle among companies participating in a supply chain. The formation of the total income and its consequent redistribution are calculated taking into account the role of the technological platform automating these procedures, which therefore becomes a party to the inter-company business project of a supply chain in the alternative roles, as feasible in business practice, of Blockchain-as-a-Service and Blockchain-as-a-Partner. The approach is implemented on Hyperledger Fabric, the most widespread platform for private and consortium blockchains. We compare and justify this design choice with the alternative given by public blockchains, with specific attention to Ethereum.
NFTs (Non-Fungible Tokens) have experienced an explosive growth and their record-breaking prices have been witnessed. Typically, the assets that NFTs represent are stored off-chain with a pointer, e.g., multi-hop URLs, due to the costly on-chain storage. Hence, this paper aims to answer the question: Is the NFT-to-Asset connection fragile? This paper makes a first step towards this end by characterizing NFT-to-Asset connections of 12,353 Ethereum NFT Contracts (6,234,141 NFTs in total) from three perspectives, storage, accessibility and duplication. In order to overcome challenges of affecting the measurement accuracy, e.g., IPFS instability and the changing availability of both IPFS and servers' data, we propose to leverage multiple gateways to enlarge the data coverage and extend a longer measurement period with non-trivial efforts. Results of our extensive study show that such connection is very fragile in practice. The loss, unavailability, or duplication of off-chain assets could render value of NFTs worthless. For instance, we find that assets of 25.24% of Ethereum NFT contracts are not accessible, and 21.48% of Ethereum NFT contracts include duplicated assets. Our work sheds light on the fragility along the NFT-to-Asset connection, which could help the NFT community to better enhance the trust of off-chain assets.
ĐŃŃĐ°ĐťŃĐš ĐĐžŃОв, Oleksandr Manzhai, Ye. V. Panchenko
The mechanism of Ethereum transactions analysis during the prevention and investigation of criminal offenses based on the study of modern experience in this area has been proposed. The directions of cryptocurrency use by offenders have been revealed. The relationship between the decrease of the cash market and the increase in the use of cryptocurrencies has been described. The state of legal regulation of cryptocurrencies in Ukraine has been studied. The insufficient regulation of the issue of handling cryptocurrencies in criminal proceedings has been emphasized. The issue of impossibility to seize cryptocurrency assets during criminal investigation has been raised. The problematic issues faced by law enforcement agencies in other countries when seizing cryptocurrencies have been outlined.
 The structure and peculiarities of the cryptocurrency Ethereum circulation have been revealed. The features of the Ethereum platform and its distinctive features have been studied. The key standards that characterize the work of the Ethereum platform have been analyzed, explanations of key terms have been provided. The essential data in the blockchain for analysis have been highlighted, the procedure for accessing the Ethereum blockchain transactions has been described. Various web resources which one can access the Ethereum transaction blockchain through have been provided.
 The purpose of email mixing, the conditions under which the anonymity of the email address is lost have been revealed. Some software tools used to analyze ethereum transactions have been evaluated by experiment. Automation of searching and building a schema of relations of different identifiers of e-transactions on the example of Maltego Community Edition and Crystal Expert have been demonstrated. Additional modules that need to be installed in Maltego Community Edition to analyze the relevant transactions effectively have been described.
 It has been emphasized that when analyzing ethereum transactions, it is necessary to use not only ready-made tools, but also various scientific methods, such as identifying key criminal groups and wallets, identifying cases of money laundering using cryptocurrencies, additional address profiling, prevention of illegal behavior on the trading ethereum platform. The importance of effective analysis of cryptocurrencies for investigation has been described. The effectiveness of the Crystal Blockchain platform as a tool for analyzing Ethereum transactions in criminal investigations has been evaluated. The technical side of law enforcement training on the seizure of cryptocurrency assets has been revealed. For this purpose, it is recommended to use the so-called test networks. The mechanism of controlled transfer of cryptocurrency assets for custodial and non-custodial wallets has been proposed.
Emmanuel Joel Aikins Abakah, Guglielmo Maria Caporale, Luis A. GilâAlana
This paper assesses the impact of US policy responses to the Covid-19 pandemic on various technology-related assets such as cryptocurrencies, financial technology, and artificial intelligence stocks using fractional integration techniques. More precisely, it analyzes the behavior of the percentage returns in the case of nine major coins (BitcoinâBITC, StellaâSTEL, LitecoinâLITE, EthereumâETHE, XRP (Ripple), Dash, MoneroâMONE, NEM, TetherâTETH) and two technology-related stock market indices (the KBW NASDAQ Technology IndexâKFTX, and the NASDAQ Artificial Intelligence indexâAI) over the period 1 January 2020â5 March 2021. The results suggest that fiscal measures such as debt relief and fiscal policy announcements had positive effects on the series examined during the pandemic, when an increased mortality rate tended instead to drive them down; by contrast, monetary measures and announcements appear to have had very little impact and the Covid-19 containment measures none at all.
Ahmed Bouteska, Petr HĂĄjek, Mohammad Zoynul Abedin, Yizhe Dong
This study aims to examine whether the prices and returns of two cryptocurrencies, Dogecoin and Ethereum, are affected by Twitter engagement following the COVID-19 pandemic. We use the autoregressive integrated moving average with explanatory variables model to integrate the effects of investor attention and engagement on Dogecoin and Ethereum returns using data from December 31, 2020, to May 12, 2021. The results provide evidence supporting the hypothesis of a strong effect of Twitter investor engagement on Dogecoin returns; however, no potential impact is identified for Ethereum. These findings add to the growing evidence regarding the effect of social media on the cryptocurrency market and have useful implications for investors and corporate investment managers concerning investment decisions and trading strategies.
Majd Soud, Ilham Qasse, Grischa Liebel, Mohammad Hamdaqa
Due to the risks associated with vulnerabilities in smart contracts, their security has gained significant attention in recent years. However, there is a lack of open datasets on smart contract vulnerabilities and their fixes that allows for data-driven research. Towards this end, we propose an automated framework for mining and classifying Ethereumâs smart contract vulnerabilities and their corresponding fixes from GitHub and from the Common Vulnerabilities and Exposures (CVE) records in the National Vulnerability Database. We implemented the proposed method in a fully automated framework, which we call AutoMESC. AutoMESC uses seven of the most well-known smart contract security tools to classify and label the collected vulnerabilities based on vulnerability types. Furthermore, it collects metadata that can be used in data-intensive smart contract security research (e.g., vulnerability detection, vulnerability classification, severity prediction, and automated repair). We used AutoMESC to construct a sample dataset and made it publicly available. Currently, the dataset contains 6.7K smart contract vulnerability-fix pairs written in Solidity. We assess the quality of the constructed dataset in terms of accuracy, provenance, and relevance, and compare it with existing datasets. AutoMESC is designed to collect data continuously and keep the corresponding dataset up-to-date with newly discovered smart contract vulnerabilities and their fixes from GitHub and CVE records.
Blockchain and Internet of Things (IoT) have become one of the most researched topics in recent years. The application areas of both these fields are quite broad and one can complement the other in bringing in the best possible outcome. Keeping that in mind, this paper proposed how Blockchain can be utilized to provide a more secure and improved IoT ecosystem where the components of the ecosystem will be more protected and less susceptible to unwanted intrusions. We also considered using InterPlanetary File System (IPFS) and Hyperledger Fabric as a means of IoT data storage, which can ensure better protection compared to centralized cloud storage. We have also utilized Ethereum Smart Contracts (SC) for Proof of Concept deployment of our model. The experimental outcome as well as the cost and security analysis of our deployed Smart Contracts suggest that it is more beneficial to use Blockchain and distributed storage systems to safeguard the IoT ecosystem and its data.