A Measurement Study of the Ethereum Underlying P2P Network

This work carried out a measurement study of the Ethereum Peer-to-Peer(P2P)network to gain a better understanding of the underlying nodes.Ethereum was applied because it pioneered distributed applications,smart contracts,and Web3.Moreover,its application layer language“Solidity”is widely used in smart contracts across different public and private blockchains.To this end,we wrote a new Ethereum client based on Geth to collect Ethereum node information.Moreover,various web scrapers have been written to collect nodes’historical data fromthe Internet Archive and the Wayback Machine project.The collected data has been compared with two other services that harvest the number of Ethereumnodes.Ourmethod has collectedmore than 30% more than the other services.The data trained a neural network model regarding time series to predict the number of online nodes in the future.Our findings show that there are less than 20% of the same nodes daily,indicating thatmost nodes in the network change frequently.It poses a question of the stability of the network.Furthermore,historical data shows that the top ten countries with Ethereum clients have not changed since 2016.The popular operating system of the underlying nodes has shifted from Windows to Linux over time,increasing node security.The results have also shown that the number of Middle East and North Africa(MENA)Ethereum nodes is neglected compared with nodes recorded from other regions.It opens the door for developing new mechanisms to encourage users from these regions to contribute to this technology.Finally,the model has been trained and demonstrated an accuracy of 92% in predicting the future number of nodes in the Ethereum network.

“Half of the Node Records Are Forged?”: The Problemof Node Records Forgery in Ethereum Network

Ethereum, currently the most widely utilized smart contracts platform, anchors the security of myriad smartcontracts upon its own robustness. Its foundational peer-to-peer network facilitates a dependable node connectionmechanism, whereas an efficient data-sharing protocol constitutes as the bedrock of Blockchain network security.In this paper, we propose NodeHunter, an Ethereum network detector implemented through the application ofsimulation technology, which is capable of aggregating all node records within the network and the interconnectednessbetween them. Utilizing this connection information, NodeHunter can procure more comprehensive insightsfor network status analysis compared to preceding detection methodologies. Throughout a three-month period ofunbroken surveillance of the Ethereum network, we obtained an excess of two million node records along with overone hundred million node acquaintances. Analysis of the gathered data revealed that an alarming 49% or more ofthese node records were maliciously forged.

基于Ethereum全链构建的大数据测量分析与研究

Ethereum区块链作为分布式数据存储、点对点网络、共识算法、密码学科、智能合约等传统技术的集成创新,在全球数字经济发展中扮演重要角色的同时也带来了新的风险与挑战。通过建立本地节点实现Ethereum大数据构建,从而实现对Ethereum全链数据进行测量与分析,包括交易数据、区块数据、去中心化程度、TPS等维度分析。在风险内容分析方面,对相关内容风险内容实现了精确定位,并进行定性和定量分析。

基于Ethereum的风电能源交易方案

现有风电能源交易市场化面临着难题,如传统管理中心面对交易数量大、交易额度不确定、运营成本高、资金结算时差、交易不透明等缺陷。提出基于Ethereum的风电能源交易方案。通过风电成本利润控制算法和购电最优算法实现风电成本最小化和用户最优购电价。通过Ethereum交易处按照撮合最优算法实现双方去中心化交易。通过联盟链搭建智能合约实现区域风电成本管控、用户购电价格市场化机制、撮合交易管控机制三部分。仿真结果表明该方案具有良好的管理性、可控性、高效性。

Enhancement of UAV Data Security and Privacy via Ethereum Blockchain Technology

Unmanned aerial vehicles(UAVs),or drones,have revolutionized a wide range of industries,including monitoring,agriculture,surveillance,and supply chain.However,their widespread use also poses significant challenges,such as public safety,privacy,and cybersecurity.Cyberattacks,targetingUAVs have become more frequent,which highlights the need for robust security solutions.Blockchain technology,the foundation of cryptocurrencies has the potential to address these challenges.This study suggests a platform that utilizes blockchain technology tomanage drone operations securely and confidentially.By incorporating blockchain technology,the proposed method aims to increase the security and privacy of drone data.The suggested platform stores information on a public blockchain located on Ethereum and leverages the Ganache platform to ensure secure and private blockchain transactions.TheMetaMask wallet for Ethbalance is necessary for BCT transactions.The present research finding shows that the proposed approach’s efficiency and security features are superior to existing methods.This study contributes to the development of a secure and efficient system for managing drone operations that could have significant applications in various industries.The proposed platform’s security measures could mitigate privacy concerns,minimize cyber security risk,and enhance public safety,ultimately promoting the widespread adoption of UAVs.The results of the study demonstrate that the blockchain can ensure the fulfillment of core security needs such as authentication,privacy preservation,confidentiality,integrity,and access control.

Blockchain Merkle-Tree Ethereum Approach in Enterprise Multitenant Cloud Environment

This research paper puts emphasis on using cloud computing with Blockchain(BC)to improve the security and privacy in a cloud.The security of data is not guaranteed as there is always a risk of leakage of users’data.Blockchain can be used in a multi-tenant cloud environment(MTCE)to improve the security of data,as it is a decentralized approach.Data is saved in unaltered form.Also,Blockchain is not owned by a single organization.The encryption process can be done using a Homomorphic encryption(HE)algorithm along with hashing technique,hereby allowing computations on encrypted data without the need for decryption.This research paper is composed of four objectives:Analysis of cloud security using Blockchain technology;Exceptional scenario of Blockchain architecture in an enterprise-level MTCE;Implementation of cipher-text policy attribute-based encryption(CPABE)algorithm;Implementation of Merkle tree using Ethereum(MTuE)in a Multi-tenant system.Out of these four objectives,the main focus is on the implementation of CP-ABE algorithm.CP-ABE parameters are proposed for different levels of tenants.The levels include inner tenant,outer tenant,Inner-Outer-Tenant,Inner-Outer-External-Tenant,Outer-Inner-Tenant,External-Outer-Inner-Tenant and the parameters such as token,private key,public key,access tree,message,attribute set,node-level,cipher-text,salting which will help in providing better security using CP-ABE algorithm in a multitenant environment(MTE)where tenants can be provided with different levels of security and achieved 92 percentage of authenticity and access-control of the data.

融合Ethereum和IPFS加密算法的分布式数据存储传输研究

现阶段分布式网络出现可靠性低、安全性低等问题,构建相应的存储运输保障机制具有积极的意义。研究提出CP-ABE数据加密方案,该方案以Ethereum网络作为分布式网络系统架构,将IPFS作为数据存储网络,通过密文半解密算法和密钥转换算法改进CP-ABE算法实现第三方加密。研究结果表明,相较于其他数据加密方案,CP-ABE数据加密方案在属性组数为90时,加密时间和解密时间分别最短和最长,相应数值分别为1.68s和2.62s,且CP-ABE数据加密方案的解密时间不会随着请求者数量的增加而增加。研究所提出的CP-ABE数据加密方案能够大幅度提升分布式存储运输的数据安全。

基于Ethereum的房地产供应链系统设计与实现

以太坊区块链技术是当今互联网时代的一项具有创新性、革命性的综合型分布式数据库账本技术,具有去中介化、自治性、数据不可篡改、可追溯、可编程等特点。分析了传统房地产供应链存在的不足,运用新兴的区块链技术,设计了基于以太坊智能合约的房地产供应链系统。给出了该系统的系统模型,包括系统流程和系统架构两部分。具体说明了系统所涉及到的四个功能模块。展示了系统的工作流程,并讨论了区块链+房地产供应链存在的问题。对区块链应用于房地产供应链领域进行总结,旨在为未来更好地将区块链应用于房地产供应链提供参考。

Popular cryptoassets(Bitcoin,Ethereum,and Dogecoin),Gold,and their relationships:volatility and correlation modeling

Cryptoassets have experienced dramatic volatility in their prices,especially during the COVID-19 pandemic era.This pilot study explores the volatility asymmetry and correlations among three popular cryptoassets(Bitcoin,Ethereum,and Dogecoin)as well as Gold.Multiple Generalized Autoregressive Conditional Heteroskedasticity(GARCH)models are analyzed.We find that positive shocks have a greater impact on the volatility of these financial assets than negative shocks of the same magnitude,perhaps a manifestation of the fear of missing out(FOMO)effect.Our research is one of the first to use COVID-19-period volatility of financial assets(in-sample data)to forecast their later COVID-19-period volatility(out-of-sample data).This forecast accuracy is compared to that produced by forecasts using the same out-of-sample data and a longer in-sample data.Our results indicate that generally,the larger in-sample dataset gives a higher forecast accuracy though the smaller in-sample dataset is from the same regime as the out-of-sample data.We also evaluate the correlations among the assets using the Dynamic Conditional Correlation(DCC)framework and find that there is an elevated positive correlation between Gold and Bitcoin during the past two years.The Gold-Bitcoin correlation hit its peak during the peak of the COVID-19 pandemic and then fell back to around zero in July 2021 when the pandemic crisis eased.Unsurprisingly,there is a strong positive correlation among the cryptocurrencies.Pairwise correlation among all four assets was stronger during the COVID-19 pandemic.Such continuing analysis can inform portfolio asset allocation as well as general financial policy decisions.

基于Ethereum智能合约的安全策略分析

智能合约是代码和数据的集合,一旦部署便无法更改,且其自身持有金融属性,若出现安全漏洞问题将会造成巨大损失,可见编写出安全可靠的智能合约是至关重要的。为此,基于Ethereum平台研究并分析智能合约的安全漏洞,总结了几种易见的安全漏洞,包括可重入漏洞、整数溢出漏洞、拒绝服务(denial of service,DoS)漏洞、时间戳依赖漏洞、交易序列依赖漏洞等;针对上述合约的漏洞进行详细的原理分析和场景复现,提出了相应的预防安全策略并通过实验进行有效性验证;最后分析并比较了几种主流的智能合约安全漏洞检测工具。

Aparecium:understanding and detecting scam behaviors on Ethereum via biased random Walk

Ethereum's high attention,rich business,certain anonymity,and untraceability have attracted a group of attackers.Cybercrime on it has become increasingly rampant,among which scam behavior is convenient,cryptic,antagonistic and resulting in large economic losses.So we consider the scam behavior on Ethereum and investigate it at the node interaction level.Based on the life cycle and risk identification points we found,we propose an automatic detection model named Aparecium.First,a graph generation method which focus on the scam life cycle is adopted to mitigate the sparsity of the scam behaviors.Second,the life cycle patterns are delicate modeled because of the crypticity and antagonism of Ethereum scam behaviors.Conducting experiments in the wild Ethereum datasets,we prove Aparecium is effective which the precision,recall and F1-score achieve at 0.977,0.957 and 0.967 respectively.

Assessing the impact of network factors and Twitter data on Ethereum's popularity

In March 2021,we witnessed a surge in Bitcoin price.The cause seemed to be a tweet by Elon Musk.Are other blockchains as sensitive to social media as Bitcoin?And more precisely,could Ethereum's popularity be explained using social media data?This work aims to explore the determinants of Ethereum's popularity.We use both data from Etherscan to retrieve the relevant historic Ethereum factors and Twitter data.Our sample consists of data ranging from 2015 to 2022.We use Ordinary Least Squares to assess the relationship between these factors(Ethereum characteristics and Twitter data)and Ethereum's popularity.Our findings show that Ethereum's popularity—translated here by the number of daily new addresses—is related to the following elements:the Ether(ETH)price,the transaction fees,and the polarity of tweets related to Ethereum.The results could have multiple practical implications for both researchers and practitioners.First of all,we believe that it will enable readers to better understand the technology of Ethereum and its stake.Secondly,it will help the community identify pointers for anticipating or explaining the popularity of existing or future platforms.And finally,the results could help in understanding the factors facilitating the design of future platforms.

Investigating the impact of structural and temporal behaviors in Ethereum phishing users detection

The recent surge of Ethereum in prominence has made it an attractive target for various kinds of crypto crimes.Phishing scams,for example,are an increasingly prevalent cybercrime in which malicious users attempt to steal funds from a user’s crypto wallet.This research investigates the effects of network architectural features as well as the temporal aspects of user activities on the performance of detecting phishing users on the Ethereum transaction network.We employ traditional machine learning algorithms to evaluate our model on real-world Ethereum transaction data.The experimental results demonstrate that our proposed features identify phishing accounts efficiently and outperform the baseline models by 4%in Recall and 5%in F1-score.

An empirical study on the complexity, security and maintainability of Ethereum-based decentralized applications (DApps)

The Ethereum blockchain’s smart contract is a programmable transaction that performs general-purpose computations and can be executed automatically on the blockchain.Leveraging this component,blockchain technology(BT)has grown beyond the scope of cryptocurrencies and can now be applicable in various industries other than finance.In this paper,we investigated the current trends in Ethereum-based decentralized applications(DApps)to be able to categorize and analyze the DApps to measure the complexity of smart contracts behind them,their level of security and their correlation to the maintainability of the DApps.We leveraged the source code analysis,security analysis,and the developmental metadata of the DApps to infer this correlation.Based on our findings,we concluded that the maintainability of Ethereum DApps is proportional to the code size,number of functions,and,most importantly,the number of outgoing invocations and statements in the smart contracts.

Robust clustering of Ethereum transactions using time leakage from fixed nodes

Ethereum has received increasing attention as the first blockchain platform to support smart contracts.Data mining has become an important tool for analyzing Ethereum transactions.However,existing methods have the disadvantage of covering partial transactions and being vulnerable to privacy-enhancing techniques.In this paper,we propose a scheme for transaction correlation with the node as an entity,which can cover all transactions while being resistant to privacy-enhancing techniques.Utilizing timestamps relayed from N fixed nodes to describe the network properties of transactions,we cluster transactions that enter the network from the same source node.Experimental results show that our method can determine with 97%precision whether two transactions enter the network from the same source node.

基于双层孪生神经网络的区块链智能合约分类方法

当前通过深度学习方法进行区块链智能合约分类的方法越来越流行,但基于深度学习的方法往往需要大量的样本标签数据去进行有监督的模型训练,才能达到较高的分类性能。该文针对当前可用智能合约数据集存在数据类别不均衡以及标注数据量过少会导致模型训练困难,分类性能不佳的问题,提出基于双层孪生神经网络的小样本场景下的区块链智能合约分类方法:首先,通过分析智能合约数据特征,构建了可以捕获较长合约数据特征的双层孪生神经网络模型;然后,基于该模型设计了小样本场景下的智能合约训练策略和分类方法。最后,实验结果表明,该文所提方法在小样本场景下的分类性能优于目前最先进的智能合约分类方法,分类准确率达到94.7%,F1值达到94.6%,同时该方法对标签数据的需求更低,仅需同类型其他方法约20%数据量。

A Systematic Review and Performance Evaluation of Open-Source Tools for Smart Contract Vulnerability Detection

With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges such as slow updates,usability issues,and limited installation methods.These challenges hinder the adoption and practicality of these tools.This paper examines smart contract vulnerability detection tools from 2016 to 2023,sourced from the Web of Science(WOS)and Google Scholar.By systematically collecting,screening,and synthesizing relevant research,38 open-source tools that provide installation methods were selected for further investigation.From a developer’s perspective,this paper offers a comprehensive survey of these 38 open-source tools,discussing their operating principles,installation methods,environmental dependencies,update frequencies,and installation challenges.Based on this,we propose an Ethereum smart contract vulnerability detection framework.This framework enables developers to easily utilize various detection tools and accurately analyze contract security issues.To validate the framework’s stability,over 1700 h of testing were conducted.Additionally,a comprehensive performance test was performed on the mainstream detection tools integrated within the framework,assessing their hardware requirements and vulnerability detection coverage.Experimental results indicate that the Slither tool demonstrates satisfactory performance in terms of system resource consumption and vulnerability detection coverage.This study represents the first performance evaluation of testing tools in this domain,providing significant reference value.

EtherClue:Digital investigation of attacks on Ethereum smart contracts

Programming errors in Ethereum smart contracts can result in catastrophic financial losses from stolen cryptocurrency.While vulnerability detectors can prevent vulnerable contracts from being deployed,this does not mean that such contracts will not be deployed.Once a vulnerable contract is instantiated on the blockchain and becomes the target of attacks,the identification of exploit transactions becomes indispensable in assessing whether it has been actually exploited and identifying which malicious or subverted accounts were involved.In this work,we study the problem of post-factum investigation of Ethereum attacks using Indicators of Compromise(IoC)specially crafted for use in the blockchain.IoC definitions need to capture the side-effects of successful exploitation in the context of the Ethereum blockchain.Therefore,we define a model for smart contract execution,comprising multiple abstraction levels that mirror the multiple views of code execution on a blockchain.Subsequently,we compare IoCs defined across the different levels in terms of their effectiveness and practicality through EtherClue,a prototype tool for investigating Ethereum security incidents.Our results illustrate that coarse-grained IoCs defined over blocks of transactions can detect exploit transactions with less computation.However,they are contract-specific and suffer from false negatives.On the other hand,fine-grained IoCs defined over virtual machine instructions can avoid these pitfalls at the expense of increased computation,which is nevertheless applicable for practical use.

基于以太坊的服装设计版权方案

现有的中心化服装设计版权体系中,低质量抄袭作品层出不穷,而作品的审核周期长且完成度不是非常高的阶段性成果又无法得到认可,使得创作者维权时往往缺乏这一时间段的时间证明。此外,作品的版权确权并不简洁,不利于版权的使用,数据本身也有遭受攻击的风险。为解决上述问题,提出了一个基于区块链、零水印和星际文件系统(IPFS)的服装设计版权方案。区块链的不可篡改特性保证了数据安全,使得其与传统版权保护流程能够有效结合。同时将区块链取代以往零水印中的可信知识产权保护(IPR)中央权威机构,消除了第三方带来的隐患。智能合约结合时间戳功能可以准确提供各步骤的线上记录,让创作者能更好提供作品从草稿到成品的时间线证明,以便在版权纠纷中维护自身权益。而感知哈希也能剔除掉抄袭作品,从抄袭作品的登记源头来控制低级抄袭作品的出现,保证创作者的著作权。实验表明,整体功能完善、安全性高,其中零水印方案具备可行性和有效性,作品登记和查询消耗的以太坊资源Gas成本低。

NFT仿冒欺诈的测量与检测技术

近年来非同质化代币(Non-Fungible Token,NFT)繁荣发展,但安全问题也日益凸显,尤其是NFT的仿冒问题.在去中心化的环境下,仿冒已有的NFT作品变得相对容易,而辨别真伪却尤其困难.本文围绕仿冒NFT的测量与仿冒检测方法的评估进行了系统深入的研究.建立了包括形式化定义、仿冒过程和仿冒特征在内的NFT仿冒威胁模型,给出了 NFT仿冒定义,分析了 NFT仿冒方式,给出了判定仿冒NFT的一般性方法.大规模采集了全球最大的NFT交易平台OpenSea上50 000个NFT项目的智能合约地址和历史交易数据,并从以太坊区块链上采集了这些NFT项目的名称、创建时间、元数据以及链下存储的NFT图像数字载体,从中选取668个交易量排名靠前的NFT项目围绕NFT仿冒问题开展了测量工作,结果表明其中95个项目被仿冒248次,交易金额超过2600万美元,足见NFT生态所面临的仿冒欺诈问题之严重.本文采用了 22种图像数据增强方法,构造了 5000个扰动较小的攻击测试样本数据集,评估了 OpenSea和知名的第三方商业检测平台Fnftf对仿冒NFT检测的鲁棒性,测试结果表明有6种图像数据增强方法构造的攻击测试样本能够轻易绕过检测,揭示了 NFT行业仿冒欺诈检测产品的脆弱性.为提高对仿冒NFT检测的鲁棒性,本文提出并实现了一种基于深度学习的NFT图像仿冒检测模型,实验表明其AUC值相较于Fnftf提升了 15.9%.