Over the past few years, with the advent of blockchain technology, there has been a massive increase in the usage of Cryptocurrencies. However, Cryptocurrencies are not seen as an investment opportunity due to the market's erratic behavior and high price volatility. Most of the solutions reported in the literature for price forecasting of Cryptocurrencies may not be applicable for real-time price prediction due to their deterministic nature. Motivated by the aforementioned issues, we propose a stochastic neural network model for Cryptocurrency price prediction. The proposed approach is based on the random walk theory, which is widely used in financial markets for modeling stock prices. The proposed model induces layer-wise randomness into the observed feature activations of neural networks to simulate market volatility. Moreover, a technique to learn the pattern of the reaction of the market is also included in the prediction model. We trained the Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) models for Bitcoin, Ethereum, and Litecoin. The results show that the proposed model is superior in comparison to the deterministic models.
The COVID-19 pandemic provided the first widespread bear market conditions since the inception of cryptocurrencies. We test the widely mooted safe haven properties of Bitcoin, Ethereum and Tether from the perspective of international equity index investors. Bitcoin and Ethereum are not a safe haven for the majority of international equity markets examined, with their inclusion adding to portfolio downside risk. Only investors in the Chinese CSI 300 index realized modest downside risk benefits (contingent on very limited allocations to Bitcoin or Ethereum). As Tether successfully maintained its peg to the US dollar during the COVID-19 turmoil, it acted as a safe haven investment for all of the international indices examined. We caveat the latter findings with a warning that Tether's dollar peg has not always been maintained, with evidence of impaired downside risk hedging properties earlier in our sample.
In Ethereum, reaching a transaction consensus costs a certain number of gases, which should be purchased by users in their self-defined gas prices. Generally, the higher the gas price, the shorter the time is spent on reaching consensus. Since the transaction gas prices still vary greatly in a block, generating a reasonable price that can make a trade-off between the consensus time and the gases cost is of great significance. In this paper, we propose a Machine Learning Regression-based gas price predicting approach (MLR), aiming to find the lowest transaction gas price in the next block for carrying out economical Ethereum transaction. Specifically, we identify five influencing factors (i.e., difficulty, block gas limit, transaction gas limit, ether price, and miner reward) from the Ethereum transacting process and resort the classic machine learning regression to build the predicting model. Our empirical study on 194,331 blocks implies that the proposed MLR approach works well and can save $17,552.2 for all transactions in the 74.9% accuracy.
For users of the Ethereum network, the gas price is a crucial parameter that determines how swiftly the decentralized consensus protocol confirms a transaction. This paper studies the statistics of the Ethereum gas price. We start with some conceptual discussion of the gas price notion in view of the actual transaction-selection strategies used by Ethereum miners. Subsequently, we provide the descriptive statistics of what we call the threshold gas price. Finally, we identify and estimate a seasonal ARIMA (SARIMA) model for predicting the hourly median of the threshold gas price.
Ethereum accelerates the transaction process through a quicker block creation design. Since the time interval between the generation of blocks is very short (about 15s), block propagation time in an inefficient network is not negligible compared with the block time interval. This lead to the production of a large number of orphan blocks. In order to solve the security problems that may be caused by the orphan block and improve the transaction processing efficiency, Ethereum introduces the uncle block mechanism, i.e., an orphan block may get part of minted reward if it gets a reference by a regular block. In this paper, we show the weakness of the uncle block mechanism. Firstly, we describe the specific differences of Ethereum selfish and stubborn mining in every state from the ones in Bitcoin. Secondly, we simulate possible attacks, and the results show that the Ethereum selfish and stubborn mining strategies not only increase the reward of an attacker but also decrease the security threshold. The security threshold refers to the proportion of the attacker's computational power that needs to be achieved in order to obtain a higher reward than he should. In a practical network congestion rate, the security threshold are weakened to 0.129 and 0.216 against the Lead stubborn mining strategy and the original selfish mining strategy, respectively. When the congestion rate is rising, the reward is increasing and the threshold is decreasing. Thirdly, possible strategies are evaluated to find out the optimal one in different settings. Fourthly, we also extend the evaluation by combining three eclipse attack strategies with selfish or stubborn mining. Most of combinations bring more advantages to an attacker than a single strategy.
We report our experience in the formal verification of the deposit smart contract, whose correctness is critical for the security of Ethereum 2.0, a new Proof-of-Stake protocol for the Ethereum blockchain. The deposit contract implements an incremental Merkle tree algorithm whose correctness is highly nontrivial, and had not been proved before. We have verified the correctness of the compiled bytecode of the deposit contract to avoid the need to trust the underlying compiler. We found several critical issues of the deposit contract during the verification process, some of which were due to subtle hidden bugs of the compiler.
In the Ethereum network, miners are incentivized to include transactions in a block depending on the gas price specified by the sender. The sender of a transaction therefore faces a trade-off between timely inclusion and cost of his transaction. Existing recommendation mechanisms aggregate recent gas price data on a per-block basis to suggest a gas price. We perform an empirical analysis of historic block data to motivate the use of a predictive model for gas price recommendation. Subsequently, we propose a novel mechanism that combines a deep-learning based price forecasting model as well as an algorithm parameterized by a user-specific urgency value to recommend gas prices. In a comprehensive evaluation on real-world data, we show that our approach results on average in costs savings of more than 50% while only incurring an inclusion delay of 1.3 blocks, when compared to the gas price recommendation mechanism of the most widely used Ethereum client.
McCorry et al. (Financial Cryptography 2017) presented the first implementation of a decentralized self-tallying voting protocol on Ethereum. However, their implementation did not scale beyond 40 voters since all the computations were performed on the smart contract. In this paper, we tackle this problem by delegating the bulk computations to an off-chain untrusted administrator in a verifiable manner. Specifically, the administrator tallies the votes off-chain and publishes a Merkle tree that encodes the tallying computation trace. Then, the administrator submits the Merkle tree root and the tally result to the smart contract. Subsequently, the smart contract transits to an intermediate phase where at least a single honest voter can contend the administrator’s claimed result if it was not computed correctly. Then, in the worst case, the smart contract verifies the dispute at the cost of an elliptic curve point addition and scalar multiplication, and two Merkle proofs of membership which are logarithmic in the number of voters. This allows our protocol to achieve higher scalability without sacrificing the public verifiability or voters’ privacy. To assess our protocol, we implemented an open-source prototype on Ethereum and carried out multiple experiments for different numbers of voters. The results of our implementation confirm the scalability and efficiency of our proposed solution which does not exceed the current block gas limit for any practical number of voters.
Ethereum is a kind of blockchain platform where developers may develop and run programs called smart contracts. It inherently relies on gas consumption within a specified allowance to constrain code execution, making every instruction along an execution path to be a location for raising an exception. In this paper, we present GasFuzzer, the first work in exploring the effects of gas allowance manipulation to expose gas-oriented exception security vulnerabilities. GasFuzzer consists of two phases. The first phase introduces a gas-greedy strategy to favor transactions having higher gas consumption for mutation to obtain test transactions with different gas consumptions. The second phase introduces a novel notion of fractional gas consumption coverage and a novel gas-leveling strategy. It applies them to mutate the gas allowances of some of these transactions resulting in the highest gas consumptions produced in the first phase followed by applying these allowance-mutated transactions together with those which remained non-mutated to fuzz test the smart contract. We report an evaluation of GasFuzzer via an experiment on 3170 real-world smart contracts deployed on the public Ethereum Blockchain between October 2017 and July 2019. The findings show that GasFuzzer with gas-greedy strategy can detect more Exceptions Disorder kind of security vulnerabilities (7 more cases) than the previous state-of-the-art black-box fuzzer, and GasFuzzer with gas-leveling strategy and gas coverage criterion can detect 6 additional cases of Exceptions Disorder security vulnerabilities, which is significant.
Ethereum is a decentralized blockchain, known as being the second most popular public blockchain after Bitcoin. Since Ethereum is decentralised the canonical state is determined by the Ethereum network participants via a consensus mechanism without a centralized coordinator. The network participants are required to evaluate every transaction starting from the genesis block, which requires a large amount of network, computing, and storage resources. This is impractical for many devices with either limited computing resources or intermittent network connectivity. To overcome this drawback Ethereum defines a light client protocol where the light client fetches the blockchain state from a node operating as a light protocol server. Light clients are unable to maintain blockchain state internally, and as a consequence can only perform partial validation on blocks. Thus they rely on the light server for full block validation and to provide the updated blockchain state. Light clients connect to multiple light servers to mitigate the risk of relying on a single potentially dishonest server. Ethereum light clients are known to suffer from a probabilistic security model, but they are widely assumed to be secure under normal operating conditions. In fact, the implicit security assumptions of light clients have not been formally characterised in the literature. We present and analyse the probabilistic security guarantees under three different adversarial scenarios. The results show that for any adversary that is able to manipulate the network, the security assurances provided by the light protocol are severely impacted, and in some cases entirely lost. These results clearly demonstrate that the assumption of normal operating conditions is insufficient to justify the security assumptions of light clients. Our work also provides insight to the security of light clients under different security parameters, allowing light client implementers to more accurately understand the potential security trade-offs.
The idea of a shared economy becomes one of the companies as an enterprise type. Especially with the advanced development of digital smart devices and the internet, several forms of the mutual economy have been advanced in accord with the need for sharing of separate income. Shareable commodity and digital content are also seeking to utilize. When digital content is used as a sharing economy, various possible threats may arise in the course of transactions, the potential for theft, alteration, and hacking of contents. This paper presents a comprehensive overview of the security and privacy of Blockchain. Blockchain promise transparent, tamper-proof and secure systems that can enable novel solutions, especially when combined with smart contracts. In this research, we proposed a content protection and transaction method using Blockchain Ethereum Technology. The encryption algorithm is incorporated in proposed system to make transparent transactions and it is also implemented on content itself to prevent from smart forgery and hacking. The experimental results signify that the proposed method has strong potential to enhance transactions transparency by minimizing the security threats in digital content transactions.
The Blockchain technology and, in particular blockchain-based cryptocurrencies, offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-tokens are permanently recorded on distributed ledgers and are publicly available. This allows us to construct a transaction graph and to assess not only its organization but to glean relationships between transaction graph properties and crypto price dynamics. The goal of this paper is to facilitate our understanding on horizons and limitations of what can be learned on crypto-tokens from local topology and geometry of the Ethereum transaction network whose even global network properties remain scarcely explored. By introducing novel tools based on Topological Data Analysis and Functional Data Depth into Blockchain Data Analytics, we show that Ethereum network (one of the most popular blockchains for creating new crypto-tokens) can provide critical insights on price changes of crypto-tokens that are otherwise largely inaccessible with conventional data sources and traditional analytic methods.