We study an auction with $m$ identical items in a context where $n$ agents can arbitrarily commit to strategies. In general, such commitments non-trivially change the equilibria by inducing a metagame of choosing which strategies to commit to. In this model, we demonstrate a strategy that an attacker may commit to that ensures they receive one such item for free, while forcing the remaining agents to enter into a lottery for the remaining items (albeit for free). The attack is thus detrimental to the auctioneer who loses most of their revenue. For various types of auctions that are not too congested, we show that the strategy works as long as the agents have valuations that are somewhat concentrated. In this case, all agents will voluntarily cooperate with the attacker to enter into the lottery, because doing so gives them a chance of receiving a free item that would have otherwise cost an amount commensurate with their valuation. The attack is robust to a large constant fraction of the agents being either oblivious to the attack or having exceptionally high valuations (thus reluctant to enter into the lottery). For these agents, the attacker may coerce them into cooperating by promising them a free item rather than entering in to the lottery. We show that the conditions for the attack to work hold with high probability when (1) the auction is not too congested, and (2) the valuations are sampled i.i.d. from either a uniform distribution or a Pareto distribution. The attack works for first-price auctions, second-price auctions and the transaction fee mechanism EIP-1559 used by the Ethereum blockchain.
Walid Mensi, Mariya Gubareva, Hee-Un Ko, Xuan Vinh Vo · 5 authors
This study investigates tail dependence among five major cryptocurrencies, namely Bitcoin, Ethereum, Litecoin, Ripple, and Bitcoin Cash, and uncertainties in the gold, oil, and equity markets. Using the cross-quantilogram method and quantile connectedness approach, we identify cross-quantile interdependence between the analyzed variables. Our results show that the spillover between cryptocurrencies and volatility indices for the major traditional markets varies substantially across quantiles, implying that diversification benefits for these assets may differ widely across normal and extreme market conditions. Under normal market conditions, the total connectedness index is moderate and falls below the elevated values observed under bearish and bullish market conditions. Moreover, we show that under all market conditions, cryptocurrencies have a leadership influence over the volatility indices. Our results have important policy implications for enhancing financial stability and deliver valuable insights for deploying volatility-based financial instruments that can potentially provide cryptocurrency investors with suitable hedges, as we show that cryptocurrency and volatility markets are insignificantly (weakly) connected under normal (extreme) market conditions.
Specialists\n\nNo crypto-trading experience required\nAvailable on the web and mobile phones\nTrade Bitcoin 360 AI, Ethereum and other computerized types of cash\nMovable computation settings\nDemo account\n\nCons\n\nLeast store of $250 to open another record\nNo nuances of past execution\n\nGo with a wise choice\n\nConcerning picking a trading stage, considering all of the available choices is huge. Bitcoin 360 AI is only one of many trading stages out there. Hence, take as much time as is expected to investigate all of the open decisions before chasing after a decision.\n\nhttps://www.btc360ai.org/
Kealan Dunnett, Shantanu Pal, Zahra Jadidi, Raja Jurdak
CTI sharing is increasingly used by organisations to strengthen security. The sensitivity of CTI has led to research on trust-based sharing, yet most existing CTI sharing approaches only support static trust-based decisions or centralised trust evaluation, limiting their scalability and lead to centralised risk. This paper proposes a blockchain-based CTI sharing framework that relies on trustless delegates for dynamic trust-based decision-making and decentralised trust evaluation. To facilitate trustless delegation, our proposal allows CTI producers to intentionally inject false data on a periodic basis into the system to audit the behaviour of delegates. Moreover, unlike existing approaches, delegates within our framework facilitate sharing of CTI directly with consumers such that scalable CTI sharing occurs. The results of a qualitative evaluation of the proposed framework's security show that it is resilient to common privacy and trust concerns. Moreover, a quantitative evaluation of a proof-of-concept prototype using Ethereum show that the proposed framework is scalable and cost-effective.
A blockchain-based decentralized application (DApp) refers to an application typically using web pages or mobile applications as the front-end and smart contracts as the back-end. The front-end of the DApp helps users generate transactions and send them to the user’s blockchain wallet. After the user signs and confirms the transaction using the blockchain wallet, the transaction will invoke the smart contract of the DApp. However, users bear the following risks when using DApps because of the potential inconsistent behaviors in DApps. First, the DApp front-end may generate incorrect transactions inconsistent with users’ intentions. Second, the smart contract may have misbehaviors when executing the transactions. Inconsistent behaviors of DApps not only lead to user confusion but also cause significant financial losses. In this paper, we proposed a novel approach to identify inconsistent behaviors of DApps on EVM-compatible blockchains by contrasting the behaviors of DApps that derived from the front-end, blockchain wallet, and smart contracts, respectively. We implemented our approach into a prototype named DAppHunter. We have applied DAppHunter on 92 real-world DApps of Ethereum and Binance Smart Chain and successfully identified 37 DApps with inconsistent behaviors. We confirmed that 35 of them are scam DApps and over 5 million blockchain addresses are at risk of becoming victims of these inconsistent DApps.
Marco Di Francesco, Lodovica Marchesi, Raffaele Porcu
Trading data sets is not easy. The owner of valuable data, once they are sold the first time, cannot be sure that they will not be copied and resold. On the other hand, the buyer, cannot be sure that the seller will not sell the same data to a competitor. The advent of blockchain technology, or DLT, can mitigate, or event solve these issues, because it can certify the data ownership, and act as a broker between seller and buyer. In this paper we present Kryptosafe, a system developed following sound software engineering practices, aimed to manage the trade of data sets taking advantage of the unique features of immutability and trustfulness of Ethereum blockchain, and of IPFS distributed DBMS. Kryptosafe allows data sellers to sell a whole encrypted data set or to show potential buyers a subset of it, allowing full access only after the sale is finalized. Using ERC721 and ERC1155 tokens, it also manages one-time sales, when the data set ownership is simply transferred to the buyer, or multiple sales of the same data set to different buyers.
We investigate the recent fee mechanism EIP1559 of the Ethereum network. Whereas previous studies have focused on myopic miners, we here focus on strategic miners in the sense of miners being able to reason about the future blocks. We derive expressions for optimal miner behavior (in terms of setting block sizes) in the case of 2-block foresight and varying degrees of hashing power. Results indicate that a sufficiently large mining pool will have enough hashing power to gain by strategic foresight. We further use a simulation study to examine the impact of both 2-block and 3-block foresight. In particular, the simulation study indicates that for realistic levels of hashing power, mining pools do not gain from being able to reason more than 2 blocks ahead. Moreover, even though the presence of strategic miners increase the variation in block sizes and potentially empty blocks, overall system throughput tend to increase slightly compared to myopic mining.
Farhana Javed, Josep Mangues‐Bafalluy, Engin Zeydan
This poster presents a use case for smart contract-based inter-provider agreements and Service Level Agreement (SLA) monitoring for 6G networks. We use chainlink oracle and InterPlanetary File System (IPFS) to monitor SLA data logs. We also provide experimental evaluations of two approaches: raw data log access in IPFS and chainlink-based log access. To understand the performance and feasibility of the proposed approaches on a public blockchain, the proposed framework is deployed on the Ethereum and Polygon testnets to measure the cost and latency for both approaches. We measure the latency as well as the total cost for comparison purposes. The maximum cost observed for the first approach is ≈ 1.4 USD, and the maximum latency observed with the first approach is ≈4 seconds in the Polygon testnet and 12 ~ 14 seconds in the Ethereum testnet. However, the second approach's latency is 30 ~ 60 seconds.
In recent years, the market for non– fungible tokens (NFTs) has been developing steadily. The concept of tokenising the uniqueness of digital elements has emerged thanks to the Ethereum blockchain token standard. This type of token is associated with digital properties in the context of the value of properties as unique identifiers. This feature has expanded the penetration of NFTs in most industries. Despite this, the NFT ecosystem is still at an early stage of development, while the technologies on which NFTs are based are entering a mature stage of development. All this generates a large amount of unsystematic information, which complicates the process of analysing and evaluating NFT projects. This article identifies opportunities for businesses to use NFTs to help modernise and digitalise their operations. It also identifies the areas by which all NFT collections are grouped and provides criteria for differentiating assets by rarity. The study also identified the impact of the minimum price and the factors that influence it during listing. Based on the results of the analysis, a six– stage analysis of NFT projects was derived, which can be used to analyse and determine the rationality of further research for investment.
Decentralization is widely recognized as a crucial characteristic of blockchains that enables them to resist malicious attacks such as the 51% attack and the takeover attack. Prior research has primarily examined decentralization in blockchains employing the same consensus protocol or at the level of block producers. This paper presents the first individual-level measurement study comparing the decentralization of blockchains employing different consensus protocols. To facilitate cross-consensus evaluation, we present a two-level comparison framework and a new metric. We apply the proposed methods to Ethereum and Steem, two representative blockchains for which decentralization has garnered considerable interest. Our findings dive deeper into the level of decentralization, suggest the existence of centralization risk at the individual level in Steem, and provide novel insights into the cross-consensus comparison of decentralization in blockchains.
Secure voting may be a crucial application of blockchain advancement, but guaranteeing the astuteness and protection of voting can be a complex issue. This record proposes a blockchain-based cloud computing appear on the Ethereum Virtual Machine (EVM) trade to address this issue. Our dissidents will utilize savvy contracts to screen vote preparation, but the votes themselves will be put away off-chain in a secure cloud capacity framework. We utilize agreement calculations based on Proof-of-Stake (PoS) measures to guarantee voting preparation choices. This degree guarantees that authorized centers can take part, so to talk, within the preparation of votes, which votes are numbered precisely. Moreover, we propose a novel cryptographic plot that permits for exact numbering of votes whereas ensuring vote mystery. Our appear is outlined to be versatile and successful in arrange to form it reasonable for large-scale voting scenarios. demonstrate. Our comes about show up to supply a flexible and able include for secure voting whereas guaranteeing voting precision and secrecy.
Ethereum is a popular blockchain that facilitates the creation of decentralized applications (dApps) and enables digital transactions to be executed without the need for a central authority. However, as in traditional markets, information asymmetry and market inefficiencies are used to the detriment of ordinary users via trading strategies that exploit “Miner Extractable Value” (MEV). We propose two extensions of Ethereum, one for proof of work (PoW), and one for proof of stake (PoS), that eliminate most forms of MEV by randomizing the execution order of transactions and hiding the content of transactions until their inclusion in a block. We simulate attack scenarios for both settings and provide detailed security properties and proofs.
Smart contracts in blockchains have been increasingly used for high-value business applications. It is essential to check smart contracts' reliability before and after deployment. Although various program analysis and deep learning techniques have been proposed to detect vulnerabilities in either Ethereum smart contract source code or bytecode, their detection accuracy and scalability are still limited. This paper presents a novel framework named MANDO-HGT for detecting smart contract vulnerabilities. Given Ethereum smart contracts, either in source code or bytecode form, and vulnerable or clean, MANDO-HGT custom-builds heterogeneous contract graphs (HCGs) to represent control-flow and/or function-call information of the code. It then adapts heterogeneous graph transformers (HGTs) with customized meta relations for graph nodes and edges to learn their embeddings and train classifiers for detecting various vulnerability types in the nodes and graphs of the contracts more accurately. We have collected more than 55K Ethereum smart contracts from various data sources and verified the labels for 423 buggy and 2,742 clean contracts to evaluate MANDO-HGT. Our empirical results show that MANDO-HGT can significantly improve the detection accuracy of other state-of-the-art vulnerability detection techniques that are based on either machine learning or conventional analysis techniques. The accuracy improvements in terms of F1-score range from 0.7% to more than 76% at either the coarse-grained contract level or the fine-grained line level for various vulnerability types in either source code or bytecode. Our method is general and can be retrained easily for different vulnerability types without the need for manually defined vulnerability patterns.
Maruf Yakubu Ahmed, Samuel Asumadu Sarkodie, Thomas Leirvik
We examine the relationship between the top five cryptos and the U.S. S&P500 index from January 2018 to December 2021. We use the novel General-to-specific Vector Autoregression (GETS VAR) and traditional Vector Autoregression (VAR) model to analyze the short- and long-run, cumulative impulse-response, and Granger causality test between S&P500 returns and the returns of Bitcoin, Ethereum, Ripple, Binance and Tether. Additionally, we used the Diebold and Yilmaz (DY) spillover index of variance decomposition to validate our findings. Evidence from the analysis suggests positive short- and long-run effects of historical S&P500 returns on Bitcoin, Ethereum, Ripple, and Tether returns--and negative short- and long-run effects of the historical returns of Bitcoin, Ethereum, Ripple, Binance, and Tether on S&P500 returns. Alternatively, evidence suggests a negative short- and long-run effect of historical S&P500 returns on Binance returns. The cumulative test of impulse-response indicates a shock in historical S&P500 returns stimulates a positive response from cryptocurrency returns while a shock in historical crypto returns triggers a negative response from S&P500 returns. Empirical evidence of bi-directional causality between S&P500 returns and crypto returns suggest the mutual coupling of these market. Although, S&P500 returns have high-intensity spillover effects on crypto returns than crypto returns have on S&P500. This contradicts the fundamental attribute of cryptocurrencies for hedging and diversification of assets to reduce risk exposure. Our findings demonstrate the need to monitor and implement appropriate regulatory policies in the crypto market to mitigate the potential risks of financial contagion.
In recent years, permissionless blockchains have gained significant attention for their ability to secure and provide transparency in transactions. The development of blockchain technology has shifted from cryptocurrency to decentralized finance, benefiting millions of unbanked individuals, and serving as the foundation of Web3, which aims to provide the next generation of the internet with data ownership for users. The rise of NFTs has also helped artists and creative workers to protect their intellectual property and reap the benefits of their work. However, privacy risks associated with permissionless blockchains have become a major concern for individuals and institutions. The role of blockchain in the transition from Web2 to Web3 is crucial, as it is rapidly evolving. As more individuals, institutions, and organizations adopt this technology, it becomes increasingly important to closely monitor the new risks associated with permissionless blockchains and provide updated solutions to mitigate them. This paper endeavors to examine the privacy risks inherent in permissionless blockchains, including Remote Procedure Call (RPC) issues, Ethereum Name Service (ENS), miner extractable value (MEV) bots, on-chain data analysis, data breaches, transaction linking, transaction metadata, and others. The existing solutions to these privacy risks, such as zero-knowledge proofs, ring signatures, Hyperledger Fabric, and stealth addresses, shall be analyzed. Finally, suggestions for the future improvement of privacy solutions in the permissionless blockchain space shall be put forward.
Benjamin Kraner, Nicolò Vallarano, Caspar Schwarz-Schilling, Claudio J. Tessone
This paper presents a study of the Poof-of-Stake (PoW) Ethereum consensus protocol, following the recent switch from Proof-of-Work (PoS) to Proof-of-Stake within Merge upgrade. The new protocol has resulted in reduced energy consumption and a shift in economic incentives, but it has also introduced new threat sources such as chain reorganizations and balancing attacks. Using a simple and flexible agent-based model, this study employs a time-continuous simulation algorithm to analyze the evolution of the blocktree and assess the impact of initial conditions on consensus quality. The model simulates validator node behavior and the information propagation throughout the peer-to-peer network of validators to analyze the resulting blockchain structure. Key variables in the model include the topology of the peer-to-peer network and average block and attestation latencies. Metrics to evaluate consensus quality are established, and means to observe the model's responsiveness to changes in parameters are provided. The simulations reveal a phase transition in which the system switches from a consensus state to a non-consensus state, with a theoretical justification presented for this observation.
To prevent DoS attacks, Ethereum assigns a fixed gas cost to every atomic operation in the EVM and the party who creates a transaction has to pay for its overall gas usage. While the gas model is successful in preventing DoS attacks, it causes significant costs in transaction fees. For example, in June-September 2022, the average daily gas usage of Ethereum was almost four million dollars. We propose a solution to minimize these fees by moving most of the execution of a contract off-chain and storing only the bare minimum on-chain. We then trigger an on-chain execution only if there is a disagreement between the parties to the contract, which is in turn only possible if at least one party is acting dishonestly. In such cases, our approach can identify and penalize the dishonest party by making them pay not only for the gas usage of their own function calls, but also calls made by other parties. Thus, it is game-theoretically irrational to behave dishonestly in this protocol. If all parties are rational, the total gas usage goes down significantly. Notably, our approach does not require a sidechain and works directly on the main Ethereum blockchain. We also provide extensive experiments over real-world Ethereum smart contracts, demonstrating that our protocol reduces their gas usage by 40.09%.
Blockchain systems often rely on rationality assumptions for their security, expecting that nodes are motivated to maximize their profits. These systems thus design their protocols to incentivize nodes to execute the honest protocol but fail to consider out-of-band collusion. Existing works analyzing rationality assumptions are limited in their scope, either by focusing on a specific protocol or relying on non-existing financial instruments. We propose a general rational attack on rationality by leveraging an external channel that incentivizes nodes to collude against the honest protocol. Our approach involves an attacker creating an out-of-band bribery smart contract to motivate nodes to double-spend their transactions in exchange for shares in the attacker's profits. We provide a game theory model to prove that any rational node is incentivized to follow the malicious protocol. We discuss our approach to attacking the Bitcoin and Ethereum blockchains, demonstrating that irrational behavior can be rational in real-world blockchain systems when analyzing rationality in a larger ecosystem. We conclude that rational assumptions only appear to make the system more secure and offer a false sense of security under the flawed analysis.
Dr. P. N. Fale, Payal Dahe, Namita Shendre, Priyanshu Khadaskar · 6 authors
Fake products create a huge negative impact in the market for both buyers and sellers. The sellers fails to deliver the product as per the consumers expectations and the consumers starts to doubt the quality and standards of the company which ultimately results in the negative marketing of the brand whose fake products are being circulated in the market. The most critical part about counterfeit products is that it can be harmful for the consumers. Since, the fake or counterfeit products are not restricted to any particular sector in the market therefore it has become important for us to detect these products and find a way to keep them out of the market. These products can be dangerous if we consider very dominating sectors of market like pharmaceutical and food supplies. To tackle such problems, we need to maintain a data, which is easily accessible to consumers where they can verify the details about the products and build a level of trust regarding the product authenticity. As we all know that no product is safe from counterfeiting due to the continuous growth in counterfeit products in the supply chain. It is degrading company's name and their profit; it also affects the customer, for example if this counterfeiting is done in pharmaceutical field, then it will directly affect the customer's health. To counter this problem, the research work has been proposed but not perfected. In this proposed approach, we will be using Blockchain technology to find genuineness of the product. Blockchain technology is generally a ledger system, which holds all the data of the transactions that take place on it. The unique thing about this technology is that the ledger that we mentioned here is a distributed ledger across a peer-to-peer network. Also, we propose a system where we store product's detail and its ownership status on architecture provided by Ethereum. We will be using QR code, which will be scanned by customer such that he/she will be able to find out the details of the product as manufacturing details, current owner etc. and will be able to determine whether the product is fake or real.
B. Subashini, Hemavathi Devarajan, Venkatesh Kaliamoorthy
Blockchains typically employ IPFS for off-chain storage of user information.Centralized management, muddled data, inaccurate data, and the simplicity of building information enclaves plague traditional traceability systems.In this research, blockchain technology is used to record and access data on Non-Perishable (NP) agricultural commodities in the distribution chain to solve the challenges above.The blockchain and IPFS both store public and private data encrypted.This lessens the burden on the blockchain and enhances information search.Blockchain technology enhances farmer-customer relationships and food supply chains by tracking food back to its source.Its secure data storage enables datadriven farming.By storing encrypted files IPFS hashes in smart contracts, IPFS secures agricultural data and addresses the blockchain storage problem.Being deployed in association with connects makes it possible for rapid financial transactions to occur with any changes made to the blockchain's data.This article analyses performance and simulates implementation in Ethereum testnets.The results show that our system protects sensitive data, supply chain data, and real-world applications by increasing the throughput and latency.
Blockchain denial of service (BDoS) and selfish mining are the two most crucial attacks on blockchain technology. A classical DoS attack targets the computer network to limit, restrict, or stop accessing the system of authorized users which is ineffective against renowned cryptocurrencies like Bitcoin, Ethereum, etc. Unlike the conventional DoS, the BDoS affects the system's mechanism design to manipulate the incentive structure to discourage honest miners to participate in the mining process. In contrast, in a selfish mining attack, the adversary miner keeps its discovered block private to fork the chain intentionally that aiming to increase the incentive of the adversary miner. This paper proposed a technique to successfully avoid BDoS and selfish mining attacks. The existing infrastructure of blockchain technology does not need to be changed a lot to incorporate the proposed solution.
G Sairam, Sankalp Kumar, Sheifali Gupta, Syed Sharique Haider
Abstract: The current healthcare system has traditionally relied on paper-based medical records or electronic medical records (EMRs) that are stored in centralized databases. This method, however, has shown to be ineffective and unreliable, causing major delays in patient care. Medical documents are frequently sent over email as a temporary fix; however, this approach is unsafe and unreliable. Data breaches or unauthorised access to sensitive patient information pose a serious danger and may have far-reaching repercussions for both patients and healthcare providers. Blockchain technology can provide a more secure, decentralized, and efficient way to store and share medical records. Blockchain offers a tamper-proof and auditable method to trace transactions while using cryptographic methods to secure the integrity and confidentiality of data, access to patient data and modifications. Patients, physicians, and hospitals may easily, anytime, anywhere, and with total safety and security, access medical records via a decentralised blockchain network. Patients have discretion over who gets access to their information and can allow healthcare professionals and organisations access to their medical records. We are employing a number of technologies, like MetaMask, Ganache, Web3JS, and Solidity, to develop a blockchain-based solution. Users may connect with blockchain networks via the browser plugin MetaMask, and a local blockchain development tool called Ganache offers a testing environment for blockchain-based apps. Programmers may interface with the Ethereum blockchain using Web3JS, a JavaScript framework, and Solidity, a language used to create smart contracts the blockchain of Ethereum. A centralised blockchain network that can safely store and distribute patient data is the predicted result of a blockchain-based system for medical record sharing. The system's interoperability and scalability may be ensured via integration with already-existing healthcare systems and databases. Medical record sharing may be automated with smart contracts, allowing for safe and auditable access based on predefined criteria and permissions.
Aims: This article investigates recent advancements in machine learning and blockchain technology for cryptocurrency price prediction. The study presents a ML system using various techniques applied to six different datasets. The findings highlight that simpler models can outperform complex ones in predicting cryptocurrency prices. Methods: The methods used in this study include applying diverse ML techniques such as LSTM, CNN, SVM, KNN, XGBoost, Astro ML, LASSO, RIDGE, linear regression, DT, and GP on six cryptocurrency datasets to predict prices. Results: The research evaluated various machine learning techniques for predicting cryptocurrency prices and reported the following RMSE values: Bitcoin prediction using Nadaraya-Watson kernel regression yielded an RMSE of 0.17, while Dogecoin prediction with linear regression resulted in an RMSE of 0.032. Ethereum price prediction using Gaussian regression achieved an RMSE of 0.02. For USD Coin, a combination of XGBoost, Gaussian regression, and Ridge techniques led to an RMSE of 0.014. Binance Coin price prediction using Gaussian regression had an RMSE of 0.032, and finally, Cardano Coin prediction employing LSTM reached an RMSE of 0.059. Conclusion: This study demonstrated the effectiveness of various machine learning techniques in predicting cryptocurrency prices. It revealed that simpler models can outperform complex ones in certain cases. The research contributes valuable insights to the field and can guide future work in cryptocurrency price prediction. The proposed model achieved promising results as evaluated by the RMSE metric.
Federico Cernera, Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini · 5 authors
In the world of cryptocurrencies, public listing of a new token often generates significant hype, in many cases causing its price to skyrocket in a few seconds. In this scenario, timing is crucial to determine the success or failure of an investment opportunity. In this work, we present an in-depth analysis of sniper bots, automated tools designed to buy tokens as soon as they are listed on the market. We leverage GitHub open-source repositories of sniper bots to analyze their features and how they are implemented. Then, we build a dataset of Ethereum and BNB Smart Chain (BSC) liquidity pools to identify addresses that serially take advantage of sniper bots. Our findings reveal 14,029 sniping operations on Ethereum and 1,395,042 in BSC that bought tokens for a total of $10,144,808 dollars and $18,720,447, respectively. We find that Ethereum operations have a higher success rate but require a larger investment. Finally, we analyze token smart contracts to identify mechanisms that can hinder sniper bots.