Cryptocurrency, a digital form of currency, has emerged as a prominent asset class with unique characteristics such as decentralization, security, and global accessibility. As the adoption of cryptocurrencies continues to grow, the need for accurate price prediction using machine learning (ML) algorithms becomes crucial for various stakeholders in the financial ecosystem. This paper presents a comprehensive approach to cryptocurrency price prediction, focusing on the following key aspects: The data for this study is collected from the Binance platform, a leading cryptocurrency exchange known for its extensive market data and liquidity. The dataset includes historical price data across different time frames, including 1-hour, 4-hour, and daily intervals. Prior to analysis, the collected data undergoes thorough preprocessing steps to ensure data quality and consistency. This process includes handling missing values, removing outliers, and standardizing data formats for further analysis. Long Short-Term machine learning model algorithm is employed for price prediction. This model is chosen for its ability to capture complex patterns and dynamics in cryptocurrency price movements. The prediction phase involves training and testing the ML model using the preprocessed data. Performance evaluation metrics such as R-squared, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean absolute percentage error (MAPE) are utilized to assess the accuracy and robustness of the prediction model across different time frames. Accurate cryptocurrency price prediction is essential for various stakeholders, including investors, traders, businesses, and regulators. It facilitates informed decision- making, risk management, market analysis, trading strategy development, business planning, and regulatory compliance in the dynamic cryptocurrency market. By addressing these key elements, this study aims to contribute to the advancement of cryptocurrency price prediction methodologies using ML techniques, thereby enhancing decision-making processes and fostering a more efficient and transparent digital asset market ecosystem.
Lucian Trestioreanu, Wazen M. Shbair, Flaviene Scheidt de Cristo, Radu State
Recent technologies such as inter-ledger payments, non-fungible tokens, and smart contracts are all fruited from the ongoing development of Distributed Ledger Technologies. The foreseen trend is that they will play an increasingly visible role in daily life, which will have to be backed by appropriate operational resources. For example, due to increasing demand, smart contracts could soon face a shortage of knowledgeable users and tools to handle them in practice. Widespread smart contract adoption is currently limited by security, usability and costs aspects. Because of a steep learning curve, the handling of smart contracts is currently performed by specialised developers mainly, and most of the research effort is focusing on smart contract security, while other aspects like usability being somewhat neglected. Specific tools would lower the entry barrier, enabling interested non-experts to create smart contracts. In this paper we designed, developed and tested Blockly2Hooks, a solution towards filling this gap even in challenging scenarios such as when the smart contracts are written in an advanced language like C. With the XRP Ledger as a concrete working case, Blockly2Hooks helps interested non-experts from the community to learn smart contracts easily and adopt the technology, through leveraging well-proven teaching methodologies like Visual Programming Languages, and more specifically, the Blockly Visual Programming library from Google. The platform was developed and tested and the results are promising to make learning smart contract development smoother.
In this paper, we propose HybridCoin, a next-generation cryptocurrency that combines the advantages of both Bitcoin and Ethereum to create a highly versatile and efficient digital asset. The goal of HybridCoin is to unite Bitcoin’s proven store of value and robust security features with Ethereum’s powerful programmability and smart contract capabilities, thereby enabling a wide range of use cases and applications. We present the design and architecture of HybridCoin, with a focus on the consensus mechanism, scalability solutions, and smart contract functionality. We also discuss the security and privacy features of the proposed cryptocurrency, along with its potential use cases in digital payments, decentralized finance (DeFi), and asset management. Furthermore, we address the environmental sustainability and energy efficiency aspects of HybridCoin, offering strategies for sustainable blockchain operations. Finally, we explore the regulatory landscape and potential challenges faced by HybridCoin, including the impact of emerging trends, technological innovations, and the rise of Central Bank Digital Currencies (CBDCs). By integrating the strengths of both Bitcoin and Ethereum, HybridCoin has the potential to revolutionize the cryptocurrency ecosystem and reshape the future of digital finance.
Cynthia Jayapal, Arunmadhavan Evr, G. S. Ajay Adithya, R Raajakishore · 5 authors
Digitization has been an important driver of change and has influenced the mode of operation of universities. However, the certificates of graduates are still paper-based and do not fit into the employer’s digitized recruitment process. The digitization of certificates has long been postponed and has not adopted to the digitized process of employers and universities. Further research on this topic shows that multiple industries will benefit from a digitized process that prevents forged certificates from degrading the value of legitimate recipients and issuing institutions. This paper aims to present a systematic literature review of current distributed ledger technology-based solutions and propose a new type of digital certificate that is hashgraph based to prevent fraud and counterfeiting.
Web3, decentralized web, is one of the buzz words brought up in the field of IT business as well as IT technology. Data ownership is supposed to be democratized by Web3, which means data is kept at its owner and the availability of the data is controlled and managed by the owner in the world of Web3. One of the mechanisms providing democratization of data ownership is InterPlanetary File System (IPFS) which leaves data as well as its meta data at its owner and only pointers to data are distributed over the network for data sharing. This paper discusses network layer support for IPFS to improve its performance. In particular, we propose to use Information Centric Networking as the network layer of IPFS to speed up data search in IPFS.
Data leads to knowledge, being considered crucial for running a successful business. Its production is dramatically increasing, generating challenges into identifying what data is worth extracting and what is not. Complementary to the data value, data marketplaces are becoming increasingly popular serving as the sources for additional data. They offer purchasing and selling capabilities for external data, simplifying data sourcing, while enabling users to navigate today's complex data world. Data marketplaces are continuously developed, serving the requirements of individuals and businesses in industries such as healthcare or telecommunications, with the top ones covering individual online activities and financial information. Above all, bank transactions have altered due to digitalization, with most consumers currently employing Embedded Finance (EmFi) applications. Given that the latter can benefit from accessing data from federated data providers in different sectors and transparent value driven approaches, the existence of EmFi data marketplaces is vital. In this manuscript, the FAME project is introduced aiming to launch a federated EmFi data marketplace. It goes beyond current data marketplaces since it supports the EmFi domain that currently lacks dedicated marketplaces, also offering: (i) a federated data catalogue, (ii) a single-entry point authorization/authentication infrastructure, (iii) a decentralized trading and monetization capability, and (iv) a set of EmFi-related trusted and energy-efficient analytics. All the capabilities of the FAME data marketplace are analysed, depicting the current market status and FAME's advancements. The manuscript concludes into the overall vision of FAME, depicting its applicability and future evaluation processes.
Blockchain mandates that every node store the whole chain’s history in order to address trust issues in the network. And the storage requirement becomes extremely high, severely affecting the chain’s scalability. To solve such a problem, many optimizations of storage have been proposed. In this paper, existing ways of blockchain storage scalability are described in two categories: off-chain and on-chain. The off-chain way is combined with various distributed and nondistributed storage systems. And on-chain is optimized by changing its block structure, storage rules, or technology. Blockchain technology with scalable storage has been applied in the medical industry. We assess and contrast the methods’ latency, security, and cost. And we point out the problems and challenges of the existing approaches and give an outlook on the future.
Vytautas Tumas, Beltrán Borja Fiz Pontiveros, Christof Ferreira Torres, Radu State
Blockchains are disrupting traditional finance by reducing the number of intermediaries and providing transparency. Blockchains, however, come with their own set of prominent issues. One such challenge is frontrunning. Attackers try to influence the transaction order so that their transaction executes before their victims' transaction. While frontrunning is a well-studied topic on Ethereum, it is unknown whether other blockchains are also susceptible to such attacks. One proposed defence strategy against frontrunning attacks is to randomize the transaction execution order. XRP Ledger is the highest-value blockchain to use such a strategy. Furthermore, it runs a Decentralized Exchange, which provides ample frontrunning opportunities. Therefore, in the context of XRP Ledger, we examine whether randomized transaction order provides sufficient protection against frontrunning. Our results show that the mechanism embedded in the XRP Ledger protocol is insufficient to prevent these attacks. We showcase two strategies to perform frontrunning attacks. The first, “naive” strategy, uses randomly generated accounts, whereas the second uses carefully selected accounts to improve the attack's success. Based on our analysis of the XRP Ledgers' historical data, we estimate that attackers could generate up to approx. 1.4M USD profit over two months, provided they succeeded to frontrun every opportunity.
Custody plays a fundamental role for organizations and individuals seeking to access crypto assets (e.g., cryptocurrencies) and decentralized finance (DeFi) applications. Custodians store private keys and approve and sign transactions. Crypto asset transactions must be performed efficiently and securely to prevent a ny losses or theft of the assets. This paper presents a survey and taxonomy of the state-of-the-art solutions for this custody problem, highlighting their various features, as well as the open challenges that must be addressed.
Smart contracts are programs with data (mutable state); stored on and executed by blockchain platforms. The transactions (or function invocations) dispatched to smart contracts often change their state. In the Ethereum blockchain, nodes (aka miners/validators) can schedule a set of transactions in any order in a block. Multiple transactions in a single block operating on a contract's shared state may yield different outcomes based on their execution order, thus creating a possibility for non-determinism and races between transactions. The resulting issue in Ethereum smart contracts is Transaction Ordering Dependency (TOD). Detecting a TOD requires identifying valid transactions affecting a contract's global/state variables which is equivalent to detecting read-after-write dependencies in race detection, and we expect it to be similarly nontrivial for human developers. In this paper, we identify various TODs, including a novel type previously undocumented in the literature. To detect these TODs, we propose an information flow analysis-based static analyzer, TODler. Our manual evaluation of 108 Ethereum smart contracts shows that TODler outperforms previously available approaches in terms of both run time and precision and also detects the novel TOD pattern identified in this paper.
Non-fungible tokens (NFTs) are digital assets that provide unique ownership and authenticity of digital media such as art, music, and collectibles.NFT Marketplace is a blockchain-based platform that enables the creation, trading, and collecting of NFTs.The platform leverages blockchain technology to ensure the authenticity and ownership of NFTs, providing a secure and transparent way to transact digital assets.In this major project report, we explore the NFT Marketplace and its underlying blockchain technology.We provide an overview of the platform's features, including the ability to tokenize any digital asset, create customizable smart contracts, and sell NFTs with low fees and instant trades.We also discuss the advantages and disadvantages of the platform, including its ease of use, potential for fraud, and scalability challenges.The Non-Fungible Tokens (NFTs) have revolutionized the digital realm, redefining the concept of ownership and trade of unique digital assets.NFTs represent one-of-a-kind tokens, each verifiably and indelibly linked to a specific digital or physical asset, encompassing diverse forms of content, including video, audio, and images.These unique tokens have paved the way for creators to monetize their digital creations while providing collectors with an innovative means to invest, trade, and showcase their multimedia NFT collections.Central to the NFT ecosystem are NFT marketplaces, digital platforms designed to facilitate the creation, sale, and management of NFTs in various multimedia formats.These marketplaces have proliferated, providing creators with the means to mint NFTs, buyers with the opportunity to acquire them, and collectors with platforms to curate and trade their diverse NFT portfolios.We explore the multifaceted world of NFT marketplaces, focusing on their pivotal role in the creation, sale, and management of video, audio, and image NFTs.We analyze the economic implications, including pricing strategies and royalties, while addressing environmental sustainability concerns associated with NFTs.Challenges and opportunities encountered within this dynamic ecosystem are critically examined, including scalability, intellectual property rights, and the emergence of decentralized NFT marketplaces.Through in-depth case studies, we offer insights into the unique features and innovative approaches adopted by leading NFT marketplaces, shedding light on the transformative potential of this digital metaverse.This report serves as a valuable resource for those seeking a comprehensive understanding of NFT marketplaces catering to video, audio, and image NFTs, emphasizing the profound impact these tokens have on the creation, trade, and experience of digital content across various media formats.Navigating this dynamic digital frontier necessitates a nuanced perspective, and our survey aims to provide a holistic view of this rapidly evolving landscape.
Data synchronization in decentralized storage systems is essential to guarantee sufficient redundancy to prevent data loss. We present SNIPS, the first succinct proof of storage algorithm for synchronizing storage peers. A peer constructs a proof for its stored chunks and sends it to verifier peers. A verifier queries the proof to identify and subsequently requests missing chunks. The proof is succinct, supports membership queries, and requires only a few bits per chunk. We evaluated our SNIPS algorithm on a cluster of 1000 peers running Ethereum Swarm. Our results show that SNIPS reduces the amount of synchronization data by three orders of magnitude compared to the state-of-the-art. Additionally, creating and verifying a proof is linear with the number of chunks and typically requires only tens of microseconds per chunk. These qualities are vital for our use case, as we envision running SNIPS frequently to maintain sufficient redundancy consistently.
A non-fungible token (NFT) is a data unit stored on the blockchain. Nowadays, more and more investors and collectors (NFT traders), who participate in transactions of NFTs, have an urgent need to assess the performance of NFTs. However, there are two challenges for NFT traders when analyzing the performance of NFT. First, the current rarity models have flaws and are sometimes not convincing. In addition, NFT performance is dependent on multiple factors, such as images (high-dimensional data), history transactions (network), and market evolution (time series). It is difficult to take comprehensive consideration and analyze NFT performance efficiently. To address these challenges, we propose NFTVis, a visual analysis system that facilitates assessing individual NFT performance. A new NFT rarity model is proposed to quantify NFTs with images. Four well-coordinated views are designed to represent the various factors affecting the performance of the NFT. Finally, we evaluate the usefulness and effectiveness of our system using two case studies and user studies.
Block chain is the name of a distributed ledger system.Cryptocurrencies are by nature volatile since their prices shift often.To track the success of crypto-currencies, a platform was therefore considered.The software will monitor cryptocurrency performance and offer data on changes in the value of cryptocurrencies.In addition to using an API to obtain the cryptocurrency data, we built the platform using some of the most wellknown programming languages, including Python.With a user-friendly Interface, the platform we designed provides us with information about the performance of crypto-currencies.We get daily updates to the cryptocurrency data, which includes price movements for the previous day and the previous week.Cryptocurrency value is also a part of this.The major reason for developing this platform was to make it simple for users to get cryptocurrency insights.We designed our user interface (UI) so that users may easily and trouble-free navigate between all of the pages.
Monika di Angelo, Thomas Durieux, João F. Ferreira, Gernot Salzer
Abstract Blockchain programs (also known as smart contracts) manage valuable assets like cryptocurrencies and tokens, and implement protocols in domains like decentralized finance (DeFi) and supply-chain management. These types of applications require a high level of security that is hard to achieve due to the transparency of public blockchains. Numerous tools support developers and auditors in the task of detecting weaknesses. As a young technology, blockchains and utilities evolve fast, making it challenging for tools and developers to keep up with the pace. In this work, we study the robustness of code analysis tools and the evolution of weakness detection on a dataset representing six years of blockchain activity. We focus on Ethereum as the crypto ecosystem with the largest number of developers and deployed programs. We investigate the behavior of single tools as well as the agreement of several tools addressing similar weaknesses. Our study is the first that is based on the entire body of deployed bytecode on Ethereum’s main chain. We achieve this coverage by considering bytecodes as equivalent if they share the same skeleton. The skeleton of a bytecode is obtained by omitting functionally irrelevant parts. This reduces the 48 million contracts deployed on Ethereum up to January 2022 to 248 328 contracts with distinct skeletons. For bulk execution, we utilize the open-source framework SmartBugs that facilitates the analysis of Solidity smart contracts, and enhance it to accept also bytecode as the only input. Moreover, we integrate six further tools for bytecode analysis. The execution of the 12 tools included in our study on the dataset took 30 CPU years. While the tools report a total of 1 307 486 potential weaknesses, we observe a decrease in reported weaknesses over time, as well as a degradation of tools to varying degrees.
Ali Menati, Yuting Cai, Rayan El Helou, Chao Tian · 5 authors
Proof-of-work computation used in cryptocurrencies has witnessed significant growth in the U.S. and many other regions around the world. One of the most significant bottlenecks for the scalable deployment of such computation is its energy demand. On the other hand, the electric energy system is increasing the need for flexibility for energy balancing and ancillary services due to the intermittent nature of many new energy resources such as wind and solar. In this work, we model the operation of a cryptomining facility with heterogeneous mining devices participating in ancillary services. We propose a general formulation for the cryptominers to maximize their profit by strategically participating in ancillary services and controlling the loss of mining revenue, which requires taking into account the disparity in the efficiency of the mining machines. The optimization formulation is considered for both offline and online scenarios, and optimal algorithms are proposed to solve these problems. As a special case of our problem, we investigate cryptominers' participation in frequency regulation, where the miners benefit from their fast-responding devices and contribute to grid stability. In the second special setting, a risk-aware algorithm is proposed to jointly minimize the cost and the risk of participating in ancillary services with homogeneous mining devices. Simulation results based on real-world Electric Reliability Council of Texas (ERCOT) traces show more than 20\% gain in profit, highlighting the advantage of our proposed algorithms.
Currently, Gasper, the implemented consensus protocol of Ethereum, takes between 64 and 95 slots to finalize blocks. Because of that, a significant portion of the chain is susceptible to reorgs. The possibility to capture MEV (Maximum Extractable Value) through such reorgs can then disincentivize honestly following the protocol, breaking the desired correspondence of honest and rational behavior. Moreover, the relatively long time to finality forces users to choose between economic security and faster transaction confirmation. This motivates the study of the so-called single slot finality protocols: consensus protocols that finalize a block in each slot and, more importantly, that finalize the block proposed at a given slot within such slot. In this work we propose a simple, non-blackbox protocol that combines a synchronous dynamically available protocol with a partially synchronous finality gadget, resulting in a consensus protocol that can finalize one block per slot, paving the way to single slot finality within Ethereum. Importantly, the protocol we present can finalize the block proposed in a slot, within such slot.
Blockchain technology is one of the hottest internet techniques by far, and the NFT is a novel digital collection based on blockchain technology. In 2017, two interesting applications emerged on Ethereum, CryptonPunks, and CryptonKitties, As CryptonKitties is an example, each kitty has a unique DNA number, taking on a different appearance and temperament. These two applications have revolutionary significance for the non-fungible token proposal and practical scenario application. In this experiment, We used the Ethereum platform in the Solidity language to implement a set of digital currency systems based on NFT. It’s named CoinCoin.Smart contracts are the way we implement all the functions. Through the smart contract, it can realize its minting, trading, and other functions. The implementation of functions will be simulated on the Remix platform. Mastering NFT’s smart contract could be able to provide a new ecosystem for the encryption industry and have a profound impact on the digital asset across multiple industries, such as games, art, and digital assets. Our system can provide a reference for the implementation of the NFT smart contract and bring experiences for the further deeper research.
The Synchronic Web is a highly scalable notary infrastructure that provides tamper-evident data provenance for historical web data. In this document, we describe the applicability of this infrastructure for web archiving across three envisioned stages of adoption. We codify the core mechanism enabling the value proposition: a procedure for splitting and merging cryptographic information fluidly across blockchain-backed ledgers. Finally, we present preliminary performance results that indicate the feasibility of our approach for modern web archiving scales.
Abstract: This paper suggests a decentralised web hosting solution based on Web3 Storage and the Interplanetary File System (IPFS). The IPFS platform is used to host websites and store data. To guarantee that the source codes of the websites and users’ data are preserved over the long term, all storage miner nodes on the IPFS network provide the pinning service. Websites must use an encryption technique for data storage that protects user privacy. The suggested model integrates the Web3 Storage and IPFS networks to create a platform that offers decentralised site hosting. The suggested platform guarantees the privacy, availability, and integrity.
Millions of smart contracts have been deployed onto the Ethereum platform, posing potential attack subjects. Therefore, analyzing contract binaries is vital since their sources are unavailable, involving identification comprising function entry identification and detecting its boundaries. Such boundaries are critical to many smart contract applications, e.g. reverse engineering and profiling. Unfortunately, it is challenging to identify functions from these stripped contract binaries due to the lack of internal function call statements and the compiler-inducing instruction reshuffling. Recently, several existing works excessively relied on a set of handcrafted heuristic rules which impose several faults. To address this issue, we propose a novel neural network-based framework for EVM bytecode Function Entries and Boundaries Identification (neural-FEBI) that does not rely on a fixed set of handcrafted rules. Instead, it used a two-level bi-Long Short-Term Memory network and a Conditional Random Field network to locate the function entries. The suggested framework also devises a control flow traversal algorithm to determine the code segments reachable from the function entry as its boundary. Several experiments on 38,996 publicly available smart contracts collected as binary demonstrate that neural-FEBI confirms the lowest and highest F1-scores for the function entries identification task across different datasets of 88.3 to 99.7, respectively. Its performance on the function boundary identification task is also increased from 79.4% to 97.1% compared with state-of-the-art. We further demonstrate that the identified function information can be used to construct more accurate intra-procedural CFGs and call graphs. The experimental results confirm that the proposed framework significantly outperforms state-of-the-art, often based on handcrafted heuristic rules.
A non-fungible token (NFT) references a data store location, typically, using a URL or another unique identifier. At the minimum, a NFT is expected to guarantee ownership and control over the tokenised asset. However, information stored on a third party data store may be copied and stolen. We propose a solution to give control back to the information owner by storing encrypted content on the data store and providing additional security against hacks and zero day exploits. The content on our data store is never decrypted or returned to its owner for decryption during rekeying. Also, the key size in our protocol does not increase with each rekeying. With this, we reduce the synchronisation steps and maintain a bounded key size.
Ye Lu, Caihua Liu, Meng Zhao, Xiaodong Duo · 7 authors
Transaction throughput is an important indicator of blockchain performance. However, many current smart contract virtual machines have low efficiency in executing smart contract transactions, which results in low transaction throughput of the blockchain. Some transaction parallel execution approaches have effectively improved transaction execution efficiency, but there are still problems such as low parallelism and large-scale transaction rollbacks. Therefore, we propose FSC, a fast execution approach for smart contract transactions based on fine-grained read-write analysis. The key idea is to obtain fine-grained read-write information from the smart contract compiler, and use this read-write information to group transactions at the blockchain platform layer, thereby improving the parallelism of transaction execution. FSC mainly includes three parts of work: 1) a fine-grained analysis method for read-write information at the contract state variable level based on the contract compiler; 2) a series of merging rules for merging read-write items; 3) a method to handle conflicting transactions. Our evaluation results show that FSC can accelerate the execution of smart contract transactions. Compared with before, FSC can reduce the overall transaction execution latency by 58.1%. In addition, FSC can shorten the longest execution path by 89.9% when the variation in transaction grouping latency is within 5.5%.