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.
Kate Murray, Andrea Rossi, Diego Carraro, Andrea Visentin
Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on statistical, machine learning (ML), and deep learning (DL) approaches, but the literature is limited. Indeed, it is narrow because it focuses on predicting only the prices of the few most famous cryptos. In addition, it is scattered because it compares different models on different cryptos inconsistently, and it lacks generality because solutions are overly complex and hard to reproduce in practice. The main goal of this paper is to provide a comparison framework that overcomes these limitations. We use this framework to run extensive experiments where we compare the performances of widely used statistical, ML, and DL approaches in the literature for predicting the price of five popular cryptocurrencies, i.e., XRP, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and Monero (XMR). To the best of our knowledge, we are also the first to propose using the temporal fusion transformer (TFT) on this task. Moreover, we extend our investigation to hybrid models and ensembles to assess whether combining single models boosts prediction accuracy. Our evaluation shows that DL approaches are the best predictors, particularly the LSTM, and this is consistently true across all the cryptos examined. LSTM reaches an average RMSE of 0.0222 and MAE of 0.0173, respectively, 2.7% and 1.7% better than the second-best model. To ensure reproducibility and stimulate future research contribution, we share the dataset and the code of the experiments.
The issuing and verification of higher education certificates, including all higher education documents, still functions in a costly and inappropriately bureaucratic manner. Blockchain technology provides a more secure and consistent way to revolutionize the widely used generalized mechanisms and system concepts. In this paper, the most necessary requirements are examined regarding a blockchain-based higher education system, based on the most well-known research papers. Moreover, the opportunities of working on an education system by maintaining a decentralized structure organization are recommended as well. This paper recommends the most suitable blockchain scaling solution for the architecture of an education system which uses the most state-of-the-art EVM (Ethereum virtual machine) compatible approach to implement the higher education system with all the predefined requirements. It is proven that the explained smart contract-based higher education system, which uses zkEVM (zero-knowledge Ethereum virtual machine), consists of all necessary functionalities and satisfies all predefined requirements. In fact, the recommended system, by using a modular blockchain structure, implements all the functionality and capability of the examined related works in one system, namely GDPR (General Data Protection Regulation), which is compatible and more secure.
Supply chains have evolved into dynamic, interconnected supply networks, which increases the complexity of achieving end-to-end traceability of object flows and their experienced events. With its capability of ensuring a secure, transparent, and immutable environment without relying on a trusted third party, the emerging blockchain technology shows strong potential to enable end-to-end traceability in such complex multitiered supply networks. This paper aims to overcome the limitations of existing blockchain-based traceability architectures regarding their object-related event mapping ability, which involves mapping the creation and deletion of objects, their aggregation and disaggregation, transformation, and transaction, in one holistic architecture. Therefore, this paper proposes a novel 'blueprint-based' token concept, which allows clients to group tokens into different types, where tokens of the same type are non-fungible. Furthermore, blueprints can include minting conditions, which, for example, are necessary when mapping assembly processes. In addition, the token concept contains logic for reflecting all conducted object-related events in an integrated token history. Finally, for validation purposes, this article implements the architecture's components in code and proves its applicability based on the Ethereum blockchain. As a result, the proposed blockchain-based traceability architecture covers all object-related supply chain events and proves its general-purpose end-to-end traceability capabilities of object flows.
Abstract: Cryptocurrencies are the best example of Blockchain. And the Blockchain is a decentralized network where they are multiple nodes that control the network. Bitcoin established itself as the first decentralised cryptocurrency in 2009. Bitcoin revolutionized the crypto world and all other cryptocurrencies other than bitcoin such as Ethereum, Ripple, etc. are called altcoins. In the crypto market, the price of bitcoin reflects the other cryptocurrencies. Therefore, our intention is to create prediction models which uses deep learning concepts for bitcoin and to forecast its future price.
Abstract: The idea of sharing economy provides rise to distinctive concepts and develops innovative businesses. This text aims to relate the good town concept by introducing the good transport system and explores the opportunities of adopting blockchain technology in ridesharing services. Blockchain technology could be a distributed, suburbanized public ledger that allows peer-topeer transactions in a very secure means with no third party. This proposes a blockchain-based framework from the prevailing centralized framework for a ride-sharing service and implements a similar as a suburbanized application (DApp) primarily based on good contracts on Ethereum Blockchain. exploitation good contracts facilitate the users with automated transactions, removes the intermediaries, and allows varied activities to be carried out safely and firmly. Implementation of good contracts is finished exploitation of the Solidity programming language. This DApp uses the min matching algorithmic program to match riders requesting rideshare to avoid wasting total travel distance. With the overwhelming growth in the usage of crypto currencies, good contracts usage in applications as projected during this paper will rework the sharing economy.
One of the main issues over the ages has been the lack of water. The water supply networks are now being affected by the increased deployment of IOT technology and devices. The development of a trustworthy, auditable, and transparent traceable system like Blockchain is the product of creative research. Blockchain, a distributed ledger technology, introduces a fresh, creative strategy for the development of centralized, distrust-free systems. The use of blockchain technology eliminates corruption since it can keep track of every transaction. Blockchain has built-in capabilities for fault tolerance, immutability, transparency, and full transaction tracing for records that are saved, as well as for digital asset representation and transaction executions. The blockchain-based water management system described in this study is completely decentralized, with IoT devices collecting data as it is setup and adding it to the blockchain. We use the Ethereum blockchain to represent a supplier to consumer use case in our blockchain-based water management system. We also talk about the network's participants' duties in the water management system. Finally, we assessed the benefits of blockchain-based water management over existing ones and showed how it might benefit contemporary society.
Çalışmanın amacı kripto para piyasasına öncülük eden Bitcoin ile seçilmiş altcoinler arasındaki ilişkinin ve nedenselliğin varlığını tespit ederek yatırımcıların kararlarına ışık tutmaktır. Bu amaç doğrultusunda çalışmada, Bitcoin (BTC) ile Ethereum(ETH), Binance Coin (BNB), Cardano (ADA) , Litecoin (LTC), Tron (TRX), Stellar (XLM), IOTA altcoinlerinin arasındaki eşbütünleşme ve nedensellik ilişkisi araştırılmıştır. Uzun dönemli ilişkinin var olup olmadığı Engle Granger Eşbütünleşme yöntemiyle, kısa dönemli ilişkinin var olup olmadığı Sıradan En Küçük Kareler Yöntemiyle (OLS), nedensellik ilişkisinin var olup olmadığı ise Toda Yamamato Nedensellik yöntemiyle test edilmiştir. Çalışma kapsamında veri seti olarak 14.06.2018-01.12.2021 tarihleri arasında kripto para birimlerinin günlük kapanış fiyatları kullanılmıştır. Araştırma sonucunda Engle Granger Testine göre BTC ile seçilen altcoinler arasında uzun dönemli eşbütünleşme ilişkisi bulunamamıştır. OLS testine göre bağımsız ve bağımlı değişkenler arasında kısa dönemli ilişkinin var olduğu sonucuna ulaşılmıştır. Toda Yamamato Testine göre BTC’den ADA, ETH, IOTA, TRX, XLM alt coinlerine doğru granger nedensellik olduğu, BTC’den BNB ve LTC coinlerine doğru granger nedensellik olmadığı ve BTC’den etkilenmedikleri sonucuna ulaşılmıştır.
As the number of smart cars grows rapidly, vehicular crowdsensing (VCS) is gradually becoming popular. In a VCS infrastructure, sensing devices and computing units hold on smart cars as well as cloud servers form an IoT-edge-cloud continuum to perform real-time sensing tasks. In order to encourage the smart cars to participate in the real-time VCS process, blockchain technology can be combined with VCS to provide an automated incentive for VCS data trading without relying on trusted third parties. However, directly using blockchain to enforce the VCS data trading process incurs expensive service fees and participants still can conduct various misbehavior. In this article, we propose a secure blockchain-based data trading system for VCS named BTT system to address the above issues. In particular, we first integrate the blockchain-based data trading process with a lightweight privacy-preserving truth discovery algorithm to ensure the accuracy of sensing data while preserving data privacy. We then propose a gas-aware optimization mechanism to minimize the gas consumption of the data trading process. Finally, we carefully design a distributed judgment mechanism to regulate all participants to behave correctly in the data trading process. To demonstrate the practicability of our design, we implement a prototype of the BTT system deployed on an Ethereum test network and conduct extensive simulations.
Abstract In areas of activity where the notion of accountability is strong, the adoption of artificial intelligence (AI) is limited by the opacity and lack of understanding of its behavior, all the more so in the embedded domain where neural networks are compressed and executed on microcontrollers. While the NIST introduced in 2021 several principles allowing the AI explainability, this paper introduces a novel scheme, HistoTrust, combining secure hardware and blockchain technology to bring trust in the traceability of AI behavior and allow its explainability. HistoTrust attests in an Ethereum ledger all the relevant data produced by a physical device, especially the heuristics inferred by AI. Thus, the audition of the ledger allows security verifications and AI behavior analysis.
Open access
Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
Industrial Internet of Things (IIoT) opens up a challenging research area towards improving secure data sharing which currently has several limitations. Primarily, the lack of inbuilt guarantees of honest behavior of participating, such as end-users or cloud behaving maliciously may result in disputes. Given such challenges, we propose a fair, accountable, and secure data sharing scheme, $\textit{FairShare}$ for IIoT. In this scheme, data collected from IoT devices are processed and stored in cloud servers with intermediate fog nodes facilitating computation. Authorized clients can access this data against some fee to make strategic decisions for improving the operational services of the IIoT system. By enabling blockchain, $\textit{FairShare}$ prevents fraudulent activities and thereby achieves fairness such that each party gets their rightful outcome in terms of data or penalty/rewards while simultaneously ensuring accountability of the services provided by the parties. Additionally, smart contracts are designed to act as a mediator during any dispute by enforcing payment settlement. Further, security and privacy of data are ensured by suitably applying cryptographic techniques like proxy re-encryption. We prove $\textit{FairShare}$ to be secure as long as at least one of the parties is honest. We validate $\textit{FairShare}$ with a theoretical overhead analysis. We also build a prototype in Ethereum to estimate performance and justify comparable results with a state-of-the-art scheme both via simulation and a realistic testbed setup. We observe an additional communication overhead of 256 bytes and a cost of deployment of 1.01 USD in Ethereum which are constant irrespective of file size.
Bishenghui Tao, Hong‐Ning Dai, Haoran Xie, Fu Lee Wang
The metaverse and its underlying blockchain technology have attracted extensive attention in the past few years. How to mine, process, and analyze the tremendous data generated by the metaverse systems has posed a number of challenges. Aiming to address them, we mainly focus on modeling and understanding the blockchain transaction network from a structural identity perspective, which represents the entire network structure and reveals the relations among multiple entities. In this article, we analyze three metaverse-related systems: non-fungible token (NFT), Ethereum (ETH), and Bitcoin (BTC) from the structural-identity perspective. First, we conduct the complex network analysis of the metaverse network and obtain several new insights (i.e., power-law degree distribution, disconnection, disassortativity, preferential attachment, and non-rich-club effect). Secondly, based on such findings, we propose a novel representation learning method named structure-to-vector with random pace (SVRP) for learning both the latent representation and structural identity of the network. Thirdly, we conduct node classification and link prediction tasks with the integration of graph neural networks (GNNs). Empirical results on three real-world datasets demonstrate that our proposed SVRP outperforms other existing methods in multiple tasks. In particular, our SVRP achieves the highest node classification accuracy (Acc) (99.3$\%$) and$F$1-score (96.7$\%$) while only requiring original non-attributed graphs.
शौनक साहू, प्रो. दिनेश कुमार तिवारी, Ankita Ankita, Brunda S P · 5 authors
Abstract: Electronic voting, or e-voting, has been used in various forms since the 1970s. B. Increase efficiency and reduce errors. However, wide adoption of such systems remains a challenge, especially in terms of improving resilience to potential failures. Blockchain is a disruptive technology of our time and promises to improve the overall resilience of electronic voting systems. The democratic system is fundamentally based on the right to vote that Allow individuals within the community to express their opinions. While voter turnout has declined in recent years, concerns about the integrity, security, and accessibility of the current voting system have increased. Electronic voting was introduced to address these concerns. However, it is not cost-effective and requires full oversight by a central authority. Blockchain is an emerging decentralized and decentralized technology that promises to improve many aspects of many industries. Extending e-voting to blockchain technology could be a solution to alleviating current concerns about e-voting. In this paper, I proposed a voting system that leverages the Ethereum blockchain and smart contracts to achieve voter management and verifiable voting records.
Rajeev Kumar Gupta, Vedant Chawla, R. K. Pateriya, Piyush Kumar Shukla · 6 authors
The threat of cyber-attacks is ever increasing in today’s society. There is a clear need for better and more effective defensive tools. Intrusion detection can be defined as the detection of anomalous behavior either in the host or in the network. An intrusion detection system can be used to identify the anomalous behavior of the system. The two major tasks of intrusion detection are to monitor data and raise an alert to the system administrators when an intrusion takes place. The current intrusion detection system is incapable of tackling sophisticated attacks which take place on the entire network containing large number of nodes while maintaining a low number of login attempts on each node in the system. A collaborative intrusion detection system (CIDS) was designed to remove the inefficiency of the current intrusion detection system which failed to detect coordinated distributed attacks. The main problem in the CIDS is the concept of trust. Hosts in the network need to trust the data sent by other peers in the network. To bring in the concept of trust and implement the proof-of-concept, blockchain was used. Pluggable authentication modules (PAM) were also used to track login activity securely before an intruder could modify the login activity. To implement blockchain, an Ethereum-based private blockchain was used.
With the rapid increase in the number of Internet of Things (IoT) devices in recent years, massive amounts of sensitive IoT data are being generated and transmitted over the Internet. Despite its growing adoption in various fields, IoT security remains a major challenge requiring further research. IoT authentication is an essential security mechanism for building trust in IoT systems. However, conventional authentication approaches use expensive cryptographic primitives that do not align with the resource-constrained nature of IoT devices. Furthermore, centralized authentication schemes have proven to be inapplicable for cross-domain authentication and do not limit the scalability of IoT networks. Recently, blockchain technology has been applied to building decentralized authentication between IoT devices. Nevertheless, most existing blockchain-based authentication approaches incur high overhead in IoT computation, storage, and energy consumption. Authentication time is another critical issue in real-time IoT systems. When numerous IoT authentication requests are transferred to the blockchain, an additional time delay is imposed, in addition to the high computational cost of the blockchain caused by the consensus mechanism. This study proposes a hybrid centralized and blockchain-based authentication architecture for IoT systems. Edge servers are deployed to provide centralized authentication for associated IoT devices. A blockchain network of centralized edge servers is then established to ensure decentralized authentication and verification of IoT devices that belong to different and heterogeneous IoT systems. Lightweight cryptographic methods are implemented to achieve efficient authentication, in which limiting the consumption of IoT resources is required. The architecture is demonstrated using a local Ethereum blockchain network. The results indicate that the proposed method achieves significant improvements in terms of computation cost, execution time, and power consumption for IoT compared with centralized and blockchain-based authentication schemes. A security analysis proves the ability of our architecture to mitigate attacks and satisfy the IoT security requirements.
<p><em><span lang="EN-US">Cryptocurrencies like Bitcoin, Ethereum and are high volatility digital commodities, so using them as an alternative to trading investments is very dangerous. Volatility analysis, portfolio formation, and implementation forecasts need to be carried out to minimize risk levels and risk management to help investors/traders make decisions. If you estimate, its use is suitable for analyzing the returns and volatility of cryptocurrencies. The Metode used in this study are methods of converting data into returns, detecting constancy with the ADF test, and normality testing with the JarqueBera test. The result of this study is that there are differences in the rate of return on bitcoin and ethereum coins every year, in these two crypto coins have experienced a very significant increase from before the covid 19 pandemic to covid 19. The result of this study is that there are differences in the rate of return on bitcoin and ethereum coins every year, in these two crypto coins have experienced a very significant increase from before the covid 19 pandemic to covid 19.</span></em></p>
Blanka Łęt, Konrad Sobański, Wojciech Świder, Katarzyna Włosik
Stablecoins are a relatively recent phenomenon in the cryptocurrency market, gaining prominence particularly since 2018. These digital currencies are usually pegged to nonvolatile underlying assets, offering a solution to the problem of the high price volatility of nonstable cryptocurrencies such as Bitcoin. Therefore, stablecoins seem attractive to participants in the cryptocurrency market, especially in times of turmoil. This paper aims to measure the spillover effect of shocks in volatile cryptocurrencies (Bitcoin, Ethereum, Litecoin and the Cryptocurrencies Index) on the activity of investors in the stablecoin market (Tether, USD Coin, Binance USD, DAI, Paxos, Huobi USD, and Gemini USD). Using the spectral representation of variance decomposition, the paper measures the strength of the spillover effect and investigates whether the cryptocurrency market processes information rapidly or slowly. The results suggest that shocks in the volatile cryptocurrency market moderately drive the popularity of stablecoins. The spillover effects on stablecoin popularity are short-lived, as they are observed mostly within up to 3 days of the shock. The findings indicate that investors use stablecoins as safe haven assets after bad news in the volatile cryptocurrency market; however, they react more strongly to news related to individual cryptocurrencies and not so intensively to the general sentiment in the cryptocurrency market. Investors with larger capital are more resistant to shocks.
Mahesh Kumar, Lavish Kumar, Jai Jai, Yogesh Sharma
Counterfeit drugs are a major and growing concern globally, particularly during pandemics. One of the reasons for this is the inefficiency of the supply chain system in the pharmaceutical industry, which can make it difficult to trace the ownership of drugs from manufacturer to wholesaler, distributor, and pharmacist before it reaches the customer. In this study, we have evaluated different proposed blockchain-based supply chain management systems. The system that uses Hyperledger Fabric ensures data sharing, storage, transparency, and traceability in every stage of the supply chain. Meanwhile, the Ethereum architecture makes use of smart contracts to manage the communication between sender and receiver. The primary aim of the research is to improve the safety of pharmaceutical products and streamline supply chain operations with the most effective architecture. In addition to the benefits of transparency and traceability that blockchain technology can provide, it can also help to improve the overall efficiency and security of the supply chain. For example, by using smart contracts, automated processes can be put in place for the transfer of ownership and payment for drugs, reducing the need for manual intervention and reducing the risk of errors or fraud. Additionally, by creating a tamper-proof record of all transactions, blockchain technology can provide an auditable trail of the movement of drugs through the supply chain, which can be helpful for regulatory compliance and quality assurance. Furthermore, blockchain technology can also support the implementation of new business models in the pharmaceutical industry such as digitalized provenance records and tokenization of drugs to provide more transparency, security, and trust for the customer. It's worth noting that implementing blockchain technology in the pharmaceutical supply chain will require collaboration and cooperation between stakeholders in the industry, including manufacturers, wholesalers, distributors, and regulators. With the right approach and collaboration, blockchain technology has the potential to revolutionize the way that the pharmaceutical industry operates, making the supply chain more efficient, secure, and reliable, while also helping to reduce the risk of counterfeit drugs and protect public health.
BACKGROUND: Wearable devices have limited ability to store and process such data. Currently, individual users or data aggregators are unable to monetize or contribute such data to wider analytics use cases. When combined with clinical health data, such data can improve the predictive power of data-driven analytics and can proffer many benefits to improve the quality of care. We propose and provide a marketplace mechanism to make these data available while benefiting data providers. OBJECTIVE: We aimed to propose the concept of a decentralized marketplace for patient-generated health data that can improve provenance, data accuracy, security, and privacy. Using a proof-of-concept prototype with an interplanetary file system (IPFS) and Ethereum smart contracts, we aimed to demonstrate decentralized marketplace functionality with the blockchain. We also aimed to illustrate and demonstrate the benefits of such a marketplace. METHODS: We used a design science research methodology to define and prototype our decentralized marketplace and used the Ethereum blockchain, solidity smart-contract programming language, the web3.js library, and node.js with the MetaMask application to prototype our system. RESULTS: We designed and implemented a prototype of a decentralized health care marketplace catering to health data. We used an IPFS to store data, provide an encryption scheme for the data, and provide smart contracts to communicate with users on the Ethereum blockchain. We met the design goals we set out to accomplish in this study. CONCLUSIONS: A decentralized marketplace for trading patient-generated health data can be created using smart-contract technology and IPFS-based data storage. Such a marketplace can improve quality, availability, and provenance and satisfy data privacy, access, auditability, and security needs for such data when compared with centralized systems.
Miodrag J. Mihaljević, Milica Knežević, Dragan Urošević, Lianhai Wang · 5 authors
This paper considers the problem of data access control when the subscribers are IoT devices with initialization that cannot be updated during the entire life cycle. A generic framework and a particular instance for conditional data access control within IoT are proposed. The generic framework is based on the employment of a dedicated secret key-based broadcast encryption scheme where encrypted credentials for conditional data access is available in the blockchain and encrypted data subject to conditional access are available in an off-chain source of streaming data. Reduction of the keys management overhead in comparison with a straightforward decryption keys delivery is experimentally illustrated. An instance of the proposed framework built over the Ethereum blockchain platform is developed and experimentally evaluated.
Théodore Conrad, Arthur Vinciguerra, Guillaume Méroué
Constant-product market making functions were first introduced by Hayden Adams in 2017 to create Uniswap, a decentralised exchange on Ethereum. This enables users to exchange assets at any given rate. Some variations such as Balancer and Curve were later introduced. In this paper, we analyse the maths that rule this type of protocol. We show that splitting a trade in multiple smaller trades does not impact the final exchange rate. We also show that the protocol is safer and more profitable when no one recompounds their fees.
Bello Musa Yakubu, Majid Iqbal Khan, Abid Khan, Farhana Jabeen · 5 authors
Smart home devices are vulnerable to a variety of attacks. The matter gets more complicated when a number of devices collaborate to launch a colluding attack (e.g. Distributed-Denial-of-Service (DDoS)) in a network (e.g., Smart home). To handle these attacks, most studies have hitherto proposed authentication protocols that cannot necessarily be implemented in devices, especially during Device-to-Device (D2D) interactions. Tapping into the potential of Ethereum blockchain and smart contracts, this work proposes a lightweight authentication mechanism that enables safe D2D interactions in a smart home. The Ethereum blockchain enables the implementation of a decentralized prototype as well as a peer-to-peer distributed ledger system. The work also uses a single server queuing system model and the authentication mechanism to curtail DDoS attacks by controlling the number of service requests in the system. The simulation was conducted twenty times, each with varying number of devices chosen at random (ranging from 1 to 30). Each requester device sends an arbitrary request with a unique resource requirement at a time. This is done to measure the system's consistency across a variety of device capabilities. The experimental results show that the proposed protocol not only prevents colluding attacks, but also outperforms the benchmark protocols in terms of computational cost, message processing, and response times.
The most well-known cryptocurrency, Bitcoin, is one of many that are sweeping the globe. Along with them, fungible tokens traded on numerous centralised or decentralised exchanges are different from non-fungible tokens traded on NFT marketplaces. The majority of NFTs are currently digital, and in the future, producers may innovate in this area to allow for more innovative user experiences. The blockchain and its NFTs can provide fantastic chances for artists and content producers to profit from their work. The NFT Marketplace intends to be at the centre of all these fantastic use cases for NFTs by giving users a platform to produce and exchange non-fungible tokens. NFTs offer a wide range of application cases. Automated Market Makers, which are essentially decentralised markets for crypto-tokens and offer users three core operations—depositing crypto tokens in exchange for AMM shares, performing a dual operation in which shares are obtained in exchange for base tokens, and exchanging two tokens for one another—are some of the main applications of DeFi. This conceptual research discusses AMMs that are already in existence on the Ethereum blockchain and their developments, including the AMM that is now being created on the Tezos blockchain. The goal of this study is to present a thorough understanding of blockchain technology and all of its practical uses, including voting, trading NFTs, etc. It then focuses on how NFTs are traded on various platforms before aiming for improved NFT trading marketplaces, namely Automated Market Makers on various blockchains like Ethereum and Tezos.
Abstract The Internet of Vehicles (IoV) is the next phase in the evolution of vehicular ad hoc networks (VANETs).Multiple types of Smart Networks exists in our surrounding.i.e., Wireless Sensor Networks (WSNs), Crowd Sensing Networks (CSNs), and Internet of Vehicles, etc A VANET is a collection of mobile nodes (vehicles) that share data through ad hoc on-demand connections. Vehicle Tracking is one of the uses of IOV(Internet of Vehicles) and Vehicle Security is one of the major issues for all vehicle owners. On a vehicle, there are various on-board sensors that sense a vehicle’s motion and the surrounding environment. On-board sensors can also warn drivers about approaching vehicles, speeding, and slippery road conditions. The main aim of the paper is to provide solutions for False Data Injection Attack by Integration of Blockchain Based IPFS-Trust Management System with ML SVR Regression Model. Due to Network Assaults and Threats under Vanet System, the safety of the drivers is under stake and Critical. A rogue node can send out erroneous messages, causing unavoidable scenarios. We first filter the received data from Vehicles creating false traffic jam warning messages using the Machine learning SVR Regression Model where data is created and split into train and test data. We used Machine learning supervised algorithm to find whether the vehicle is a legitimate vehicle or an attacker vehicle and the result is validated using the parameters like Accuracy, Loss Rate, Precision, Recall, and F-Test Score. Algorithm Implementation results show that the FDIA attack strategy achieves a better performance than the without using ML algorithm of SVR Regression Model based attack strategy in Predicting the Vanet Security. Also, we studied the various ways to mitigate the impact of false data injection into the network through a compromised node. Users can access the system through DApp, an Ethereum-distributed application, and manage their vehicle data.