This paper studies how a simple approach based on fuzzing testing can help authors of Solidity contracts to accurately estimate the gas cost of services specified in a contract. Our fuzzer creates a private blockchain and randomly generates transactions. Such an environment is meant to simulate large scale behavior that may be seen in a public blockchain. Our fuzzer handles Ethereum starting and target endpoints in a transaction to accommodate requirements expressed in financial contracts. By comparing the gas computation made by the Ethereum Solidity compiler and the actual consumption during our fuzzing, we are able to find discrepancies between predicted and real gas consumption. Our findings are beneficial to transaction authors to correctly predict the computing resources of Ethereum miners.
Token models introduced by Distributed Ledger Systems (DLS) such as Bitcoin or Ethereum enabled a fundamental shift in the economic structures of our society. This led not only to a large number of different token models but also to a multitude of development strategies for them. However, only a few of existing tokens proved a significant success on the market. The reason for that is a missing unified understanding of parameters required for success and the lack of standardization in token economics in general. This paper derives a unified token economics taxonomy considering regulatory authorities, practitioners, and academics. The taxonomy encompasses five dimensions which are essential for classifying token economics. Finally, this work derives eight universal success parameters of a token model and in a broader sense of DLS. The clear taxonomy and parameters will enable a more efficient and expedient approach for token development.
The author uses data about transactions on Ethereum as sources for studying the relationship between the historic of transactions for a given address and the amount of gas consumed for a transaction. The author combines data about transactions, and blocks to predict the gas usage for a transaction. Specifically, how much gas will be consumed for the next transaction, given the initiator’s transaction history. The results demonstrate the value of considering the transaction history for gas usage predictions.
Ethereum is arguably the second most popular cryptocurrency-based network after Bitcoin, both make use of the distributed ledger technology known as blockchain. The blockchain-based networks are considered to be secure, but the level of provided security is proportional to the number of connected nodes, the number of influential nodes and the supported amount of hash power. Thus, the knowledge of the network properties and nodes behavior is useful to protect the network from the possible attacks such as double spending attacks, DDoS attacks, 51% attacks, and Sybil attacks. In this paper, we propose nodes discovery mechanism, which performs a P2P links discovery on Ethereum main-network. For that, we developed the Search-node, a modified version of Ethereum Client that search for all participating nodes in the network, store the nodes identification in the Bucket, and then process the peer discovery method. We analyze the collected data to discover the relationship between nodes, heavily-connected nodes, nodes geo-distribution and provide network snapshots, as well as some data related to security issues and possible attacks over the influential nodes. Our results show that approximately 300,000 nodes are connected over Ethereum network, and among these roughly 139 nodes show a high-degree.
For the management of cryptocurrencies or cryptographic tokens, many users employ a software wallet that facilitates the interaction with a blockchain in general or with on-chain programs (smart contracts) in particular. While many blockchain wallets execute their core program code off-chain, some wallets implement core functionality on-chain as smart contracts with the intent to increase trust and security by using transparent and verifiable execution.In this work, we investigate smart contracts for wallets with regard to the functionality that makes use of cryptographically secured blockchain technology. We focus on wallet contracts deployed on Ethereum, as it is the most prominent platform for tokens and smart contracts with readily available data. We aim at a better understanding of this frequently deployed group of smart contracts by analyzing characteristics of wallet contracts and grouping them into six types. To this end, we present approaches to identify wallet contracts by analyzing source code, bytecode, and execution traces extracted from transaction data. Moreover, we investigate usage scenarios and patterns. From the derived data, we extract blueprints for wallets and compile a ground truth. We provide numbers and temporal perspectives regarding the creation and use of wallets.
Malicious activities such as scams and frauds have imposed high costs for financial systems. The advent of blockchain-based cryptocurrencies such as Ethereum provides unprecedented characteristics. On one hand, the pseudonymity of the blockchain allows criminals to hide their actual identities, which is an appealing feature for conducting malicious activities. On the other hand, the public data of blockchain sets forth the opportunity for comprehensive forensic analysis. In this paper, we present a novel framework to identify malicious entities in the Ethereum blockchain network. The proposed framework composes of an efficient method for extracting a set of features from the Ethereum blockchain data to represent transactional behavior of entities. Our proposed solutions for detecting malicious entities employ variations of Logistic Regression, Support Vector Machine, Random Forest, and other ensemble methods such as Stacking and AdaBoost Classifier. The ensemble methods show high performance with F1score of 0.996 in average. The results also imply that the proposed method of feature extraction is fairly efficient in presenting the network characteristics.
Ethereum is the largest blockchain platform that supports smart contracts. Users deploy smart contracts by publishing the smart contract's bytecode to the blockchain. Since the data in the blockchain cannot be modified, even if these contracts contain bugs, it is not possible to patch deployed smart contracts with code updates. Moreover, there is currently neither a comprehensive classification framework for Ethereum smart contract bugs, nor detailed criteria for detecting bugs in smart contracts, making it difficult for developers to fully understand the negative effects of bugs and design new approaches to detect bugs. In this paper, to fill the gap, we first collect as many smart contract bugs as possible from multiple sources and divide these bugs into 9 categories by extending the IEEE Standard Classification for Software Anomalies. Then, we design the criteria for detecting each kind of bugs, and construct a dataset of smart contracts covering all kinds of bugs. With our framework and dataset, developers can learn smart contract bugs and develop new tools to detect and locate bugs in smart contracts. Moreover, we evaluate the state-of-the-art tools for smart contract analysis with our dataset and obtain some interesting findings: 1) Mythril, Slither and Remix are the most worthwhile combination of analysis tools. 2) There are still 10 kinds of bugs that cannot be detected by any analysis tool.
O dinheiro é indispensável para o ser humano e apresenta constantes evoluções. Com o avanço da tecnologia, diversos meios de pagamento surgiram e dentre eles é possível destacar as criptomoedas, um assunto recente que tem sido apresentado em diversos meios, sobretudo, como uma possibilidade de investimento. Nesse contexto, o presente trabalho tem como objetivo analisar as características tecnológicas chaves das três criptomoedas que ocupam, atualmente, os maiores índices de capitalização de mercado, sendo elas: Bitcoin, Ethereum e XRP. Para atingir tal objetivo, assumiu-se nesta pesquisa um posicionamento de natureza qualitativa. Aplicou-se técnicas descritivas para retraçar os históricos e a descrição de suas tecnologias chave. Como resultados, a presente pesquisa demonstrou que as criptomoedas apresentam objetivos diferentes, no entanto, utilizam-se da mesma tecnologia de transação, a peer-to-peer. As tecnologias de blockchain, mineração e proof-of-work são utilizadas de maneiras distintas por cada uma das criptomoedas. Por fim, considera-se que trabalhos como esse são importantes por colocarem luz nas tecnologias implementadas. Como limitações, ressalta-se o estudo em três casos. Abstract Money is indispensable for humans and is constantly evolving. With the advancement of technology, several payment methods have emerged and among them it is possible to highlight cryptocurrencies, a recent subject that was presented in various media, mainlyas a possibility of investment. Therefore, this paper aims to analyze the three cryptocurrencies that occupy the highest market capitalization indexes, namely: Bitcoin, Ethereum and XRP. To achieve this goal, a qualitative approach was assumed in this research. Descriptive techniques were applied to retrace the histories and the description of their key technologies. As a result, the present research demonstrated that cryptocurrencies have different objectives, however, it uses the same peer-to-peer transaction technology. Blockchain, mining and proof-of-work technologies are used in different ways. Finally, it is considered that works like this are important because they shed light on the implemented technologies. Limitations include the application in three cases. Keywords:Tecnologies; Criptocurrencies; Blockchain; Bitcoin; Payment.
This thesis addresses computer security problems in: Access Control, Ethereum Smart Contracts, Cloud VM Scheduling, and Logic Locking. These problems are solved using polynomially timed reductions to 2 complexity classes: PSPACE-Complete and NP-Complete. This thesis is divided into 2 parts, problems reduced to: Model Checking (PSPACE-Complete) and Integer Linear Programming (ILP) (NP-Complete). The PSPACE-Complete problems are: Safety Analysis of Administrative Temporal Role Based Access Control (ATRBAC) Policies, and Safety Analysis of Ethereum Smart Contracts. The NP-Complete problems are: Minimizing Information Leakage in Virtual Machine (VM) Cloud Environments using VM Migrations, and Attacking Logic Locked Circuits using a Reduction to Integer Linear Programming (ILP). \n \nIn Chapter 3, I create the Cree Administrative Temporal Role Based Access Control (ATRBAC)-Safety solver. Which is a reduction from ATRBAC-Safety to Model Checking. I create 4 general performance techniques which can be utilized in any ATRBAC-Safety solver. \n \n 1. Polynomial Time Solving, which is able to solve specific archetypes of ATRBAC-Safety policies using a polynomial timed algorithm. \n 2. Static Pruning, which includes 2 methods for reducing the size of the policy without effecting the result of the safety query. \n 3. Abstraction Refinement, which can increase the speed for reachable safety queries by only solving a subset of the original policy. \n 4. Bound Estimation, which creates a bound on the number of steps from the initial state, where a satisfying state must exist. This is directly used by the model checker's bounded model checking mode, but can be utilized by any solver with a bound limiting parameter. \n \nIn Chapter 4, I analyze ATRBAC-Safety policies to identify some of the ``sources of complexity'' which make solving ATRBAC-Safety policies difficult. I provide analysis of the sources of complexity that exists in the previously published datasets [128,90,54]. I perform analysis of Cree's performance techniques on the previous datasets. I create 2 new datasets, which are shown to be hard instances of ATRBAC-Safety. I analyze the new datasets to show how they achieve this hardness and how they differ from each other and the previous datasets. \n \nIn Chapter 5, I create a novel reduction from a Reduced-Solidity Smart Contract, subset of available Solidity features, to Model Checking. This reduction reduces Reduced-Solidity Smart Contract into a Finite State Machine and then reduces to an instance of a Model Checking problem. This provides the ability to test smart contracts published on the Ethereum blockchain and test if there exists bugs or malicious code. I perform empirical analysis on select Smart contracts. \n \nIn Chapter 6, I create 2 methods for generating instances of ATRBAC policies into Solidity Smart Contracts. The first method is the Generic ATRBAC Smart Contract. This method requires no modification before deployment. After deployed the owner is able to create, and maintain, the policy using special access functions. The special action functions are automated with code that converts an ATRBAC policy into a series of transactions the owner can run. The second method is the Baked ATRBAC Smart Contract. This method takes an ATRBAC policy and reduces it to a Smart Contract instance with no special access functions. The smart contract can then be deployed by anyone, and that person will have no special access. I perform an empirical analysis on the setup costs, transaction costs, and security each provides. \n \nIn Chapter 7, I create a new reduction from Minimizing Information Leakage via Virtual Machine (VM) Migrations to Integer Linear Programming (ILP). I compare a polynomial algorithm by Moon et. al. [71], my ILP reduction, and a reduction to CNF-SAT that is not included in this thesis. The polynomial method is faster, but the problem is NP-Complete thus that solution must have sacrificed something to obtain the polynomial time speed (unless P = NP). I show instances in which the polynomial time algorithm does not produce the minimum total information leakage, but the ILP and CNF-SAT reductions are able to. In addition to this, I show that Total Information Leakage also has a security vulnerability for non-zero information leakage using the <R,C> model. I propose an alternative method to Total Information Leakage, called Max Client-to-Client Information Leakage, which removes the vulnerability at the cost of increased total information leakage. \n \nIn Chapter 8, I create a reduction from the Key Recovery Attack on Logic Locked Circuits to Integer Linear Programming (ILP). This is a recreation of the ``SAT Attack'' using ILP. I provide an empirical analysis of the ILP attack and compare it to the SAT-Attack. I show that ``ILP Attack'' is a viable attack, thus future claims of ``SAT-Attack Resistant Logic Locking Techniques'' need to also show resistance to all potential NP-Complete attacks.
Blockchain technology, beyond cryptocurrencies, is called to be the new information exchange ecosystem due to its unique properties, such as immutability and transparency. The main objective of this work is to introduce the design of a decentralized rental system, which leverages smart contracts and the Ethereum public blockchain. The work started from an exhaustive investigation on the Ethereum platform, emphasizing the aspect of cryptography and all the technology behind this platform. In order to test the proposed scheme in a realistic use, the implementation of a web application for the rental of vehicles has been carried out. The application covers the entire vehicle rental process offered in traditional web applications, adding more autonomy and ease of use to users. Following Ethereum application development guidelines, all business logic is located in the smart contracts implemented in the Ethereum network, where these contracts control the entire vehicle rental system of customers. While this is a work in progress, the results obtained in the first proof of concept have been very promising.
Blockchain is the vehicle on which cryptocurrencies run, and it can’t be regulated by any legal entity during its operation.The huge growth in various cryptocurrency segments in 10 years has created the controversy of an inevitable bubble. A bubble can be generated either by queer herd behaviour or logical secular movement. Traces of evident bubbles have been a certainty and they take the perceived valuation of crypto to figures far away from its true value. This sudden diversion can be lethal due to the illogical, irrational propensity of regular market participants. This study observes ten cryptos under surveillance from September 2014 to August 2019. The selected ten (Monero, Bitcoin, XRP Ripple, Litecoin, Dogecoin, Monacoin, Ethereum, Bytecoin, Digibite, Potcoin) cryptocurrencies were studied for the last five years using Right Tailed ADF Test. Prominent traces of the rational bubble in all the underlying cryptocurrencies were found and have been considered for the study.
After demonetization, the emphasis was given to a cashless economy by the Government of India. Keeping in view the concept of cashless economy, the importance of Crypto currency cannot be denied. Crypto currency (CC) is a virtual currency and it works as a medium of exchange by using cryptography for security. It comprises diverse currencies such as Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC), Cardano (ADA), Neo (NEO), Stellar Lumens (XLM), and so on. Many countries like Canada, Australia, Bulgaria, Chile, Denmark, Estonia, Finland, Germany, and Luxembourg have adopted Bitcoin in order to moving towards a digital eco-system. The research was conducted to find out the awareness, perception and understanding about the functioning of bitcoin among individuals. This article is all about awareness of bitcoin amongst individuals and prospective if allowed by the Government of India.
Introduction: The notion of electronic voting has evolved over a period of time replacing the traditional system which was based on paper ballots. Several types of electronic voting systems exist, still the implementation is partial and there is a scope for improvement for making it more secure and user-friendly.Method In this paper, a proof-of-concept is presented which aims to address the issues and challenges in the electoral system, by using the concept of Ethereum blockchain and smart contracts. Result: These electronic electoral processes propose a centralized solution that can be easily tampered, thus increasing the problem of distrust in the citizens. To overcome this blockchain technology can be used for implementing mobile based electronic voting system. Blockchain technology is aiding in the development of novel digital services that are more secure and reliable. Discussion: The main objective of this paper is to depict how a feasible, secure and reliable mobile voting system can be built by implementing the concept of blockchain and smart contracts. Conclusion: The issue of security and transparency in the voting system can be addressed using blockchain technology. The present study aims to fulfil these gaps partially by providing use-case for the voting process which is based on mobile and blockchain technology.
This article proposes a self-organizing collaborative computing network with an approach to enhance the expectation of a collaborating node for joining the self-organizing network. The proposed approach relies on Ethereum cryptocurrency and Smart Contract to enhance the expectation of collaborating nodes by monetizing the services provided to the self-organizing network. Furthermore, an escrow based smart contract is formalized in the proposed framework to sustains the monetary trust issue between collaborating nodes. The proposed scheme can enforce an autonomic incentive management mechanism to any type of self-organizing networks such as self-organizing clouds, ad-hoc networks, self-organizing federated cloud networks, self-organizing federated learning networks, and self-organizing D2D networks to name a few. Considering the distributed nature of these self-organizing networks and the Ethereum blockchain network, a distributed agent-based methodology is materialized in the proposed framework. Following this, a proof of concept implementation for the general case of a self-organizing cloud is presented. Lastly, the article provides some insights into possible future directions using the proposed framework.
Jenny Alexandra Triana Casallas, Juan Manuel Cueva Lovelle, José Ignacio Rodríguez Molano
The appearance of so-called block chains or Blockchain with the promise of transforming trust and the way value is exchanged, joins the expansion of the technological capabilities of organizations to achieve higher levels of productivity and innovation. This is how Blockchain-based techniques are being applied to many fields, focusing in this article on the public sector, as a possible solution to the demands for transparency, participation and citizen cooperation that society demands; due to the possibility of disintermediation based on automated transactions and on the responsibility and security in the management of official blockchain records. This could obstruct corruption and make government services more transparent and efficient. Although, it investigates about applications in the public sector under the Blockchain system, such as transactions, agreements, property registries and innovations, developments and other assets; Special emphasis is placed on the possibility of implementing Smart Contracts (mechanisms that aim to eliminate intermediaries to simplify processes) in public procurement procedures, given that it is in this type of activity where high levels of corruption are generated. It is concluded then that Europe has the largest number of blockchain initiatives worldwide, while Latin America, except for the case of Peru, lacks this type of applications, being this continent exactly where there are the countries with the highest levels of corruption. It concludes with a recommendation to use blockchain along with smart contracts through platforms such as Ethereum or Lisk, mainly given its flexibility and current development on topics with similar functionalities.
Cryptocurrency such as Bitcoin, Ethereum etc. is increasingly well known nowadays among cryptocurrency miners and enthusiasts.Our goal is to estimate the various cryptocurrency accurately considering various parameters that impact the cost.We use various deep learning and machine learning algorithms to be able to recognize the value pattern on the closing value thus giving us the predicted price.The point of this is to determine the precision of cryptocurrency values utilizing various AI algorithms and predict their estimated prices.The dataset that we will be using contains consolidated financial information for the top 10 cryptocurrencies sorted by Market Cap which also have various attributes for each type of cryptocurrency such as open, close, high, low, value and date.Therefore, we are going to use Linear Regression, SGD Regression, Support Vector Regression, and LSTM algorithms to predict the various cryptocurrencies prices.