Vedant Sharma, Anitha Palakshappa, Syed Adil Naqvi
The work highlights exploring the usage of blockchain technology for enhancing traceability in agricultural supply chain management.The aim is to develop a secure and transparent system, which improves the easy tracking and tracing of agricultural products from the point of origin until it reaches the end consumer.Currently, Blockchain is a technology, which provides security in various fields of transactions.The work utilizes to improve supply chain efficiency, increase transparency and accountability, and enhance consumer trust in the agricultural products.The system will utilize smart contracts to automate processes and ensure compliance with regulations and standards, which improves supply chain efficiency.Smart contracts enable agreement between two parties present in the supply chain.Further, the financial transactions can be improved with the help of block chain.Additional, the work will also provide recommendations for companies and organizations looking to implement blockchain-based results in their supply chain management.The work implements an application using ganache, solidity and truffle.Ethereum block chain is used as primary infrastructure for the application.Smart contracts generated using solidity is deployed into Ethereum network using truffle.The deployment of the application in agricultural sectors improves the accountability in the field of the supply chain.The deployment in a wider range will avoid manipulation of the data.Agricultural supply chain tracing website involves the use of several tools and technologies, including Ganache, Solidity, and Truffle.The system uses the Ethereum blockchain as the underlying infrastructure to store and manage supply chain data securely and transparently.The smart contracts in the supply chain tracing system are generated using Solidity and deployed to the Ethereum network using Truffle.
Nowadays, technology is increasingly being adopted in different kinds of businesses to process, store, and share sensitive information in digital environments that include enormous numbers of users. However, this has also increased the likelihood of cyberattacks and misuse of information, potentially causing severe damage. One promising technology, which can provide the required security services with an improved level of efficiency, is blockchain. This research explores the use of Ethereum blockchain and smart contracts to create a secure and efficient quality assurance system (QAS) for academic programs. By utilizing blockchain and smart contracts, the proposed approach improves the integrity and reliability of sensitive information processed by the QAS, promotes transparency and governance, and reduces the time and effort required for quality operations. The current approach uses an additional access control layer to further enhance user privacy. Smart contracts automate various quality transactions and saves time and resources, and hence increases the efficiency of the QAS. The interplanetary file system (IPFS) is used to address the challenge of size limitations in blockchain. Additionally, this research investigates the use of various cryptographic schemes to provide robust security services at the application layer. The experimental results showed that the use of a hybrid cryptosystem relying on an Elliptic curve digital signature and AES encryption (AES_ECCDSA) outperforms other counterpartsâ cryptosystems using an RSA digital signature and AES encryption (AES_RSADSA) and Elliptic Curve Integrated Encryption Scheme (ECIES) in terms of speed. The performance results showed that AES_ECCDSA consumes 188 ms to perform the required cryptographic operations for a standard-quality document with a size of 8088 KB, compared to the 231 ms and 739 ms consumed by the AES_RSADSA and ECIES schemes, respectively. This study presents a prototype implementation of the blockchain-based QAS, which outlines the processing model and system requirements for key QAS processes. It has been found that the cost and time required for blockchain operations vary depending on the size of the input dataâa larger data size requires more time and costs more to process. The results of the current study showed that the time delay for blockchain transactions ranges from 15 to 120 s, while the cost ranges from USD 50 to USD 400. This research provides evidence that blockchain and smart contract technologies have the potential to create a secure, efficient, and trustworthy QAS environment for academic programs.
Krzysztof Gogol, Johnnatan Messias, Deborah Miori, Claudio J. Tessone ¡ 5 authors
This study quantifies the potential non-atomic MEV on Layer-2 (L2) blockchains by measuring the arbitrage opportunities between cross-rollup and DEX-CEX. Over recent years, we observe a shift in trading activities from Ethereum to rollups, with swaps on rollups occurring 2-3 times more frequently, albeit with lower trade volumes. By analyzing the costs of swap on L2s and price discrepancies cross-rollup and DEX-CEX, we identify more than 500 000 unexplored arbitrage opportunities. In particular, we find that these opportunities persist, on average, for 10 to 20 blocks, necessitating the modification of the Loss Versus Rebalancing (LVR) metric to prevent double-counting. Our findings indicate that the arbitrage opportunities in Arbitrum, Base, and Optimism range between 0.03% and 0.05% of the trading volume, while in the ZKsync it fluctuates around 0.25%.
Amir M. Ebrahimi, Bram Adams, Gustavo A. Oliva, Ahmed E. Hassan
The proxy pattern is a well-known design pattern with numerous use cases in several sectors of the software industry. As such, the use of the proxy pattern is also a common approach in the development of complex decentralized applications (DApps) on the Ethereum blockchain. Despite the importance of proxy contracts, little is known about (i) how their prevalence changed over time, (ii) the ways in which developers integrate proxies in the design of DApps, and (iii) what proxy types are being most commonly leveraged by developers. This study bridges these gaps through a comprehensive analysis of Ethereum smart contracts, utilizing a dataset of 50 million contracts and 1.6 billion transactions as of September 2022. Our findings reveal that 14.2% of all deployed smart contracts are proxy contracts. We show that proxy contracts are being more actively used than non-proxy contracts. Also, the usage of proxy contracts in various contexts, transactions involving proxy contracts, and adoption of proxy contracts by users have shown an upward trend over time, peaking at the end of our study period. They are either deployed through off-chain scripts or on-chain factory contracts, with the former and latter being employed in 39.1% and 60.9% of identified usage contexts in turn. We found that while the majority (67.8%) of proxies act as an interceptor, 32.2% enables upgradeability. Proxy contracts are typically (79%) implemented based on known reference implementations with 29.4% being of type ERC-1167, a class of proxies that aims to cheaply reuse and clone contracts' functionality. Our evaluation shows that our proposed behavioral proxy detection method has a precision and recall of 100% in detecting active proxies. Finally, we derive a set of practical recommendations for developers and introduce open research questions to guide future research on the topic.
S Mohammed Ishaq, Arpitha K M, Himanshu Choubey, Mohammed Azeemulla ¡ 5 authors
In an increasingly computerized universe, the secure administration & trading of crypto files and documents has become a critical concern. In this estimate seeks to label this issue beside creating decentralized implementation (App) that uses distributed larger technology and deep neural network models to enable secure and efficient digital asset management, with an emphasis on NFTs. The App's features include secure wallet network, NFT picture production, stamp out, a sale out, and account handling. The App's backend is built on the Goerli development network with reliability intelligent contracts, while IPFS and ReactJS/Ethers are utilized for scattered storage and client development, individually. Furthermore, the Open AI Api is used to create unique NFT picture depends on customer input. This design showcases the actual application of distributed larger technology and deep neural network models for creating Apps for assured and scattered crypto asset management. Universal, the project adds to the continuing study on blockchain-based solutions for secure digital asset management, while also emphasizing the power of distributed larger technology and deep neural network models to change the way we handle and trade crypto assets.
Elie Bouri, Mahdi Ghaemi Asl, Sahar Darehshiri, David Gabauer
Abstract This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.
Ruichao Liang, Jing Chen, Cong Wu, Kun He ¡ 9 authors
Ponzi schemes, a form of scam, have been discovered in Ethereum smart contracts in recent years, causing massive financial losses. Existing detection methods primarily focus on rule-based approaches and machine learning techniques that utilize static information as features. However, these methods have significant limitations. Rule-based approaches rely on pre-defined rules with limited capabilities and domain knowledge dependency. Using static information like opcodes for machine learning fails to effectively characterize Ponzi contracts, resulting in poor reliability and interpretability. Our research shows no significant difference between Ponzi and non-Ponzi contracts at the opcode level. Moreover, relying on static information like transactions for machine learning requires a certain number of transactions to achieve detection, which limits the scalability of detection and hinders the identification of 0-day Ponzi schemes. In this article, we propose PonziGuard , an efficient Ponzi scheme detection approach based on contract runtime behavior. Inspired by the observation that a contractâs runtime behavior is more effective in disguising Ponzi contracts from the innocent contracts, PonziGuard establishes a comprehensive graph representation called contract runtime behavior graph (CRBG), to accurately depict the behavior of Ponzi contracts. Furthermore, it formulates the detection process as a graph classification task on CRBG, enhancing its overall effectiveness. The experiment results show that PonziGuard surpasses the current state-of-the-art approaches in the ground-truth dataset, achieving a precision of 96.9%, recall of 98.2%, and F1-score of 97.5%. It also exhibits the highest level of interpretability among the current tools. We applied PonziGuard to Ethereum Mainnet and demonstrated its effectiveness in real-world scenarios. Using PonziGuard , we identified 805 Ponzi contracts on Ethereum Mainnet, which have resulted in an estimated economic loss of 281,700 Ether or approximately \($\) 500 million USD. We also found 0-day Ponzi schemes in the recently deployed 10,000 smart contracts.
Abstract Cryptocurrencies have rapidly become popular as digital assets, and as the market evolves, it is of great importance to understand their volatility and risk behavior. They present specific challenges and opportunities given that are operating within a decentralized and fast-changing ecosystem. Thus, their volatility affects risk management, investment strategies, and market stability. Cryptocurrency volatility can create both opportunities and risks. While it can provide substantial returns, it also presents challenges in terms of investment strategy, regulatory frameworks, business operations, and economic stability. As the cryptocurrency market matures, itâs likely that solutions to manage volatility will evolve, but it remains a key concern for participants in the ecosystem. In this respect, the aim of the paper is to examine the volatility behavior of the main cryptocurrencies (Bitcoin, Ethereum, and Litecoin), for a recent period, i.e. from June 2018 to June 2023. Using both traditional and advanced GARCH models, the results show that these cryptocurrencies experience periods of high and low volatility, but there is no significant asymmetry effect in their responses. This suggests a balanced risk-return profile for investors. Furthermore, there is no evidence for risk premium within the sample, that is no link between risk and return. Additionally, past volatility has a greater impact on current volatility than new information, since GARCH coefficients are significantly higher than the ARCH coefficients. These insights can help investors, policymakers, and researchers to manage the cryptocurrency markets more effectively.
This thesis introduces two novel frameworks to defend against malicious attacks in real-time andsecure interactions between smart contracts and oracles in a blockchain system. This thesis proposes FrontDef, a security system to detect malicious transactions and perform front-running to mitigate financial loss. FrontDef monitors each transaction in the pending transaction pool, detecting po- tential attacks. For suspicious transactions, it analyzes the bytecode of the target contract and assembles mimic transactions to replicate attack strategies. Using these assembled transactions, FrontDef preemptively front-runs suspicious attack transactions, preventing financial losses. Empir- ical results demonstrate that FrontDef successfully detects and assembles mimic transactions for all 24 benchmark cases, including 21 historical attacks that occurred on Ethereum and Binance Smart Chain (BSC). We also confirm that FrontDef introduces negligible overhead and does not affect the throughput of Ethereum and BSC clients. In addition, this thesis presents OVer, a framework designed to automatically analyze the behav- ior of decentralized finance (DeFi) protocols encoded in smart contracts when exposed to âskewedâ oracle inputs. OVer begins by performing symbolic analysis and constructing a constraints model based on the contract source code. Leveraging an SMT solver, it identifies parameters that ensure secure operation. Additionally, OVer generates guard statements for smart contracts utilizing or- acle values, effectively preventing oracle manipulation attacks. Empirical results show OVer can analyze all ten diverse benchmarks successfully. Current control parameters in most benchmarks prove inadequate when faced with significant oracle deviations. Existing ad-hoc mechanisms, such as introducing delays, often fall short in real-world DeFi protection. In addition, We examine the security attributes of different pricing algorithms and simulate the impact of applying smoothing filters. Drawing insights from the outcomes, we delve into the design considerations for central bank digital currency (CBDC) oracle systems.
Securing data generated from diverse sensors poses a critical challenge in contemporary applications, particularly due to the escalating volume of data and its vulnerability to security breaches. Blockchain technology has emerged as a promising solution, yet the implementation of blockchain applications entails significant costs. Assessing the feasibility of such implementations through simulation is imperative but has been hindered by a lack of details survey about the simulation tools leading to wrong choice of tools by the researchers, as every application is unique and requires specific blockchain platform as per use. This paper covers an extensive survey of 32 simulation tools used in blockchain, focusing on their advantages, limitations, platform used, and capability to evaluate performance metrics of blockchain applications. This enables the researchers to choose the correct simulation tool for their work. The work allows the programmer to test their application using the correct simulation tool, saving implementation costs and time. Furthermore, we discussed CBlockSim, a simulation tool designed specifically for blockchain applications, elucidating its functionality through a comparative study of Bitcoin and Ethereum. Our work contributes to advancing the understanding and application of simulation tools in blockchain-based sensor data security, offering insights that can significantly enhance the security posture of sensor networks.
Abstract Research purpose. This study analysed the three cryptocurrencies with the largest market capitalization: Bitcoin, Ether (cryptocurrency built upon the Ethereum project's blockchain technology), and Binance coin, which account for 60% of the total cryptocurrency market capitalization. The purpose of this research was to measure the impact of monetary policy on the price of these cryptocurrencies using an adjusted R squared. Design / Methodology / Approach. As dependent variables, we used interest rates controlled by the European Central Bank and the Federal Reserve and reports from the European Central Bank and the Federal Open Market Committee. A robust Elastic Net Regression with Autoregressive Integrated Moving Average (ARIMA) residuals machine learning approach was applied to obtain robust regression coefficients and corresponding standard errors. To ascertain the robustness of the model, a technique known as rolling window cross-validation was employed. Findings. The results of this study show that monetary policy decisions and announcements significantly impact the price of cryptocurrencies. The impact on cryptocurrencies is likely to be significant both in the period of economic stability (2018-2020) and in the period of economic shocks (2020-2022). This relationship is likely to be indirect, acting through investor sentiment. Originality / Value / Practical implications. The results of this study may be useful to monetary policymakers, as they reveal the link between their actions and the price of cryptocurrencies. Our model will also be useful for mutual fund managers and private investors, as they can anticipate the price dynamics of cryptocurrencies when assessing monetary policy frameworks.
This study investigates how decentralization and transparency offered by blockchain technology could revolutionize traditional finance. Even with the rise of well-known cryptocurrencies such as Bitcoin and Ethereum, a general understanding of blockchainâs influence on the financial industry is still lacking. We identified five major application casesâtransparent credit scoring, effective consumer identification, expedited insurance settlements, improved cybersecurity, and the emergence of decentralized financeâwhere blockchain technology is well positioned to tackle persistent issues. We show how blockchain technology may address problems such as opaque credit scoring, poor customer identity, convoluted insurance settlement procedures, and susceptibility to cyberattacks by thoroughly examining various use cases. According to our research, a greater number of traditional financial institutions need to embrace and integrate blockchain innovations into their functions to promote inclusivity, transparency, and decentralization.
This paper evaluates the performance of the Long Short-Term Memory (LSTM) deep learning algorithm in forecasting Bitcoin and Ethereum prices during the COVID-19 epidemic, using their high-frequency price information, ranging from December 31, 2019, to December 31, 2020. Deep learning (DL) techniques, which can withstand stylized facts, such as non-linearity and long-term memory in high-frequency data, were utilized in this paper. The LSTM algorithm was employed due to its ability to perform well with time series data by reducing fading gradients and reliance over time. The obtained empirical results demonstrate that the LSTM technique can predict both Ethereum and Bitcoin prices. However, the performance of this algorithm decreases as the number of hidden units and epochs grows, with 100 hidden units and 200 epochs delivering maximum forecast accuracy. Furthermore, the performance study demonstrates that the LSTM approach gives more accurate forecasts for Ethereum than for Bitcoin prices, indicating that Ethereum is more prominent than Bitcoin. Moreover, the increased accuracy of forecasting the Ethereum price made it more reliable than Bitcoin during the COVID-19 coronavirus crisis. As a result, cryptocurrency traders might focus on trading Ethereum to increase their earnings during a crisis.
Abstract Non-fungible Tokens (NFTs), represent a revolution in the digital ownership paradigm. NFTs are a kind of digital asset built on blockchain technology, most commonly the Ethereum blockchain, that validate the uniqueness and ownership of a unique digital item in question. Each NFT carries specific information or attributes that make it original and non-fungible. Unlike cryptocurrencies like Bitcoin or Ethereum, which are identical to each other, non-fungible tokens cannot be exchanged on a like-for-like basis making them non-fungible. NFTs are traded for cryptocurrencies via online trading platforms. Investment in NFTs can present a risky situation due to the large volatility of the assets in a quite short time. This article focuses on identification of key aspects that influence decision making process of potential investors who are considering buying non-fungible tokens as an investment tool in the Czech Republic. From the point of view of investment decision-making, the primary factors appear to be the expected income from the investment, its payback period, and the risk that the investor undertakes. It has been proven that there is a degree of dependence between gender and the mentioned decision-making factors. The research showed that men are more inclined to make decisions based on expected returns, while women are more likely to make decisions based on perceived risk.
The emergence of Web3, underpinned by blockchain technology, has reshaped the digital realm, ushering in a decentralized and trustless internet paradigm. In this paper, we conduct an extensive analysis of the Web3 job market, leveraging data from 2000 job postings to delineate prevalent keywords, sought-after skills, prevalent job titles, and salary determinants. Our examination reveals compelling insights into the job landscape, showcasing the dominance of technical competencies such as Ethereum proficiency and software development expertise. Among the top skills sought by employers, Ethereum (371 occurrences), React (213 occurrences), NFT (213 occurrences), Java (205 occurrences), and Rust (102 occurrences) prominently feature. Moreover, our analysis uncovers the ascendancy of specialized roles in cybersecurity, technical leadership, and project management, which command premium compensation levels. Notably, security positions emerged as the highest paying roles (average salary: $153,295.86), followed by tech lead (average salary: $121,526.32) and operations (average salary: $120,396.55). These findings offer valuable insights for job seekers, employers, educators, and policymakers navigating the evolving Web3 job landscape. By delineating key trends and challenges, our study contributes to a nuanced understanding of the transformative potential of Web3 and its implications for the future of work.
Huong Q. Nguyen, Tri Nguyen, Lauri LovĂŠn, Susanna Pirttikangas
This paper presents a fully coupled blockchain-assisted federated learning architecture that effectively eliminates single points of failure by decentralizing both the training and aggregation tasks across all participants. Our proposed system offers a high degree of flexibility, allowing participants to select shared models and customize the aggregation for local needs, thereby optimizing system performance, including accurate inference results. Notably, the integration of blockchain technology in our work is to promote a trustless environment, ensuring transparency and non-repudiation among participants when abnormalities are detected. To validate the effectiveness, we conducted real-world federated learning deployments on a private Ethereum platform, using two different models, ranging from simple to complex neural networks. The experimental results indicate comparable inference accuracy between centralized and decentralized federated learning settings. Furthermore, our findings indicate that asynchronous aggregation is a feasible option for simple learning models. However, complex learning models require greater training model involvement in the aggregation to achieve high model quality, instead of asynchronous aggregation. With the implementation of asynchronous aggregation and the flexibility to select models, participants anticipate decreased aggregation time in each communication round, while experiencing minimal accuracy trade-off.
In Ethereum, the practice of verifying the validity of the passed addresses is a common practice, which is a crucial step to ensure the secure execution of smart contracts. Vulnerabilities in the process of address verification can lead to great security issues, and anecdotal evidence has been reported by our community. However, this type of vulnerability has not been well studied. To fill the void, in this paper, we aim to characterize and detect this kind of emerging vulnerability. We design and implement AVVERIFIER, a lightweight taint analyzer based on static EVM opcode simulation. Its three-phase detector can progressively rule out false positives and false negatives based on the intrinsic characteristics. Upon a well-established and unbiased benchmark, AVVERIFIER can improve efficiency 2 to 5 times than the SOTA while maintaining a 94.3% precision and 100% recall. After a large-scale evaluation of over 5 million Ethereum smart contracts, we have identified 812 vulnerable smart contracts that were undisclosed by our community before this work, and 348 open source smart contracts were further verified, whose largest total value locked is over $11.2 billion. We further deploy AVVERIFIER as a real-time detector on Ethereum and Binance Smart Chain, and the results suggest that AVVERIFIER can raise timely warnings once contracts are deployed.
Matthew Marcellino, Arya Wicaksana, Moeljono Widjaja
The advancement of blockchain technology introduces the new concept of electronic voting systems (e-voting) that are fully anonymous, transparent, trustless, and decentralized. The limitation of blockchain-based e-voting systems is the need for initial setup to verify and validate eligible voters. This initial setup requires human intervention, which curbs the full potential and exploitation of blockchain technology. Identity authentication is crucial in voting systems to ensure the eligibility of the voters and the validity of the results. This paper proposes a hybrid approach using ZK-SNARK for identity authentication systems in blockchain-based e-voting. The proposed hybrid approach aims to maintain the benefit of blockchain technology while guaranteeing the eligibility of voters. Both on-chain and off-chain identity authentication modules are designed and developed to balance the trade-off of centralized and decentralized nature for the blockchain-based e-voting systems. The affordability of the proposed system is essential in justifying the approach's feasibility and usability. Voting systems are expected to host thousands to millions of voters, and the cost is one major consideration. The proposed system is deployed in the Ethereum blockchain network, including its sidechain and Layer 2, i.e., Avalanche, Arbitrum One, and Polygon. The gas fee required for the smart contract deployment in Ethereum is USD12.5, while the lowest gas fee is in the Polygon blockchain network for USD0.02.
Open access
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The bubbles and spikes in cryptocurrency prices increase considerably the risk on investments in these assets. In the traditional time series literature bubbles are viewed as nonstationary and non-estimable components of a process. In this paper, we adopt a different approach and consider the bubbles as inherent features of a strictly stationary causal-noncausal (mixed) Vector Autoregressive (VAR) process. This approach allows us to model and estimate the common bubbles and spikes in cryptocurrency prices. It also provides us linear combinations of cryptocurrencies that eliminate common bubbles analogously to the cointegrating vectors eliminating common trends in unit root processes. They are used to build cryptocurrency portfolios immune to the risk of common bubbles that ensure stable investment strategies. The mixed VAR model is estimated from the US Dollar prices of Bitcoin, Ethereum, Ripple, and Stellar over the period 2017â2019. We document the common bubbles and illustrate the behavior of bubble-free portfolios.
We present tail risk analysis of cryptocurrencies (Bitcoin, Ethereum and Litecoin), non-fungible tokens, stocks (FTSE 100 and S&P 500) and Gold from November 12, 2017 to March 31, 2022 using conditional model-based Value-at-Risk (VaR). We explored which model specification and distributional innovation could best capture the tail risk in these assets. Using the VaR and other risk metrics, we showed that there is no superior model/metric for capturing tail risk. We found that, for all the assets, non-Gaussian distributional assumptions best modelled the asymmetry and fat-tails in the distributions of the returns; though there was more homogeneity in the distributional assumptions for Gold unlike the other assets. Our research is crucial for internal risk modelling and may increase global investor confidence for those who blend conventional and unconventional assets. Also, this study can help investors make informed decisions about asset allocation and risk tolerance in the events of extreme market conditions. Understanding the tail risks in financial assets can help investors hedge and diversify against risk in their portfolios. The theoretical implications also show a trade-off between the different assets as the presence of tail risk reflect the potential of returns, yet possible losses in the presence of extreme events. Last, the findings reinforce the need for risk managers to re-focus their attention to a set of superior models rather than a single best model for risk assessment.
The rapid advancement of technology, alongside state-of-the-art techniques is at an all-time high. However, this unprecedented growth of technological prowess also brings forth potential threats, as oftentimes the security encompassing these technologies is imperfect. Particularly within the automobile industry, the recent strides in technology have brought about increased complexity. A notable flaw lies in the CAN-FD protocol, which lacks robust security measures, making it vulnerable to data theft, injection, replay, and flood data attacks. With the rising complexity of in-vehicular networks and the widespread adoption of CAN-FD, the imperative to safeguard the protocol has never been more crucial. This paper aims to provide a comprehensive review of the existing in-vehicle communication protocol, CAN-FD. It explores existing security approaches designed to fortify CAN-FD, demonstrating multiple multi-layer solutions that leverage modern techniques including Physical Unclonable Function (PUF), Elliptical Curve Cryptography (ECC), Ethereum Blockchain, and Smart contracts. The paper highlights existing multi-layer security measures that offer minimal overhead, optimal performance, and robust security. Moreover, it identifies areas where these security measures fall short and discusses ongoing research along with suggestions for implementing software and hardware-level modifications. These proposed changes aim to streamline complexity, reduce overhead while ensuring forward compatibility. In essence, the methods outlined in this study are poised to excel in real-world applications, offering robust protection for the evolving landscape of in-vehicular communication systems.
The openness and transparency of Ethereum transaction data make it easy to be exploited by any entities, executing malicious attacks. The sandwich attack manipulates the Automated Market Maker (AMM) mechanism, profiting from manipulating the market price through front or after-running transactions. To identify and prevent sandwich attacks, we propose a cascade classification framework GasTrace. GasTrace analyzes various transaction features to detect malicious accounts, notably through the analysis and modeling of Gas features. In the initial classification, we utilize the Support Vector Machine (SVM) with the Radial Basis Function (RBF) kernel to generate the predicted probabilities of accounts, further constructing a detailed transaction network. Subsequently, the behavior features are captured by the Graph Attention Network (GAT) technique in the second classification. Through cascade classification, GasTrace can analyze and classify the sandwich attacks. Our experimental results demonstrate that GasTrace achieves a remarkable detection and generation capability, performing an accuracy of 96.73% and an F1 score of 95.71% for identifying sandwich attack accounts.
Abstract With the rapid development of the Ethereum ecosystem and the increasing applications of decentralized finance (DeFi), the security research of smart contracts and blockchain transactions has attracted more and more attention. In particular, front-running attacks on the Ethereum platform have become a major security concern. These attack strategies exploit the transparency and certainty of the blockchain, enabling attackers to gain unfair economic benefits by manipulating the transaction order. This study proposes a sandwich attack detection system integrated into the go-Ethereum client (Geth). This system, by analyzing transaction data streams, effectively detects and defends against front-running and sandwich attacks. It achieves real-time analysis of transactions within blocks, quickly and effectively identifying abnormal patterns and potential attack behaviors. The system has been optimized for performance, with an average processing time of 0.442 s per block and an accuracy rate of 83%. Response time for real-time detection new blocks is within 5 s, with the majority occurring between 1 and 2 s, which is considered acceptable. Research findings indicate that as a part of the go-Ethereum client, this detection system helps enhance the security of the Ethereum blockchain, contributing to the protection of DeFi usersâ private funds and the safety of smart contracts. The primary contribution of this study lies in offering an efficient blockchain transaction monitoring system, capable of accurately detecting sandwich attack transactions within blocks while maintaining normal operation speeds as a full node.
Abstract We aim to identify the determinants of nonâfungible tokens (NFTs) returns. The 10 most popular NFTs based on their price, trading volume, and market capitalisation are examined. Twentyâthree potential drivers of the returns of each NFT are considered. We employ a Bayesian LASSO model which takes into account stochastic volatility and leverage effect. The results indicate that NFTs returns are primarily driven by volatility and ethereum returns. We find a weak connection between NFTs returns and conventional assets, such as stock, oil, and gold markets.