In recent years, blockchain-based cryptocurren-cies have attracted much attention. Attacks targeting cryptocurrencies and related services directly profit an attacker if successful. Related studies have reported attacks targeting configuration-vulnerable nodes in Ethereum using a method called honeypots to observe malicious user attacks. They have analyzed 380 million observed requests and showed that attacks had to that point taken at least 4193 Ether. However, long-term observations using honeypots are difficult because the cost of maintaining honeypots is high. In this study, we analyze the behavior of malicious users using our honeypot system. More precisely, we clarify the pre-investigation that a malicious user performs before attacks. We show that the cost of maintaining a honeypot can be reduced. For example, honeypots need to belong in Ethereum's P2P network but not to the mainnet. Further, if they belong to the testnet, the cost of storage space can be reduced.
The growing popularity of Blockchain networks attracts also malicious and hacking users. Effectively detecting inappropriate and malicious activity should thus be a top priority for safeguarding blockchain networks and services. Blockchain behavior analysis can be used to detect unusual account activities or time periods with network-wide irregular properties. Thus, optimized anomaly detection based on historical data is an essential task for securing transactions and services. However, processing the complete blockchain history can be slow and costly due to its large size and rapid growth. In this paper we suggest addressing this challenge by analyzing summarized blocks data structures, called sketches, rather than the entire blockchain. Sketches are common data structures used in computer systems and blockchain networks, to allow compact data representation while supporting efficient executions of particular queries. We study how sketches can be used to detect suspicious accounts or time periods without the need to maintain or go through the entire blockchain data. We design solutions for the major known attacks and conduct experiments to evaluate them based on real Ethereum data. We compare the accuracy, run-time and memory usage of our algorithms with traditional detection algorithms relying on the complete blockchain data. Our results indicate that sketch-based anomaly detection methods can provide a practical scalable solution for detecting anomalies in blockchain networks.
Smart contracts have been developed and employed in both permissioned and permissionless blockchains recently, mainly to enforce agreements among parties without the need for intermediaries. This achievement is the result of blockchain immutability which guarantees that no party can alter the conditions of an already deployed contract. However, immutability also makes patching or updating contracts impossible even when incorrectness, unfairness, or security flaws are spotted in them. So far, researchers in academia and industry have developed two main methods, data segregation and proxy storage, with six patterns to make deployed contracts upgradable. However, until now, there has been no comprehensive framework that can simultaneously offer upgradability, security resilience, and scalability features. For example, none of the existing solutions have implemented any security mechanism that can resist attacks such as the DAO one. Through extensive analysis and implementation of all these patterns, and taking state-of-the-art attacks on the Ethereum network into consideration, we propose our innovative framework, “Comprehensive-Data-Proxy pattern” which uses data segregation on the top of proxy pattern, that can completely defend against any types of Reentrancy attacks. Additionally, this solution mitigates the scalability issue of the proxy pattern. Our experiments show that the framework can address these two issues with negligible impact on performance.
Boosted by blockchain technology, the sphere of Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) is expanding globally. Although valuations of major cryptocurrencies Bitcoin and Ethereum likely will set the future of crypto-assets, the post-2020 expansion of cryptocurrency markets prompted explosive growth of DeFi- and NFT-branded altcoins. This study constructs time-series models to examine DeFi- and NFT-related cryptocurrencies and to clarify how their weekly prices fluctuated over a one-year period. Using Google Trends data, we measure how weekly Internet searches into crypto markets generally and specific branded cryptocurrencies affected price fluctuations for each coin. Results show that Bitcoin prices modeled as an exogenous variable have a positive effect on Ethereum prices. On average, brand-specific and market-level Google searches were estimated to be negative but not statistically convincing for DeFi- and NFT-branded altcoins.
Smart contracts based on the Ethereum blockchain network have attracted attention from finance, media, and academic domains. As a result, smart contracts have been targeted by cyber attackers for the purpose of cryptocurrency theft. The smart contract honeypot is a commonly used attack method. An attacker who makes a honeypot lures other weak attackers who target vulnerable contracts by seeming to have exploitable flaws. The honeypot attacker then steals cryptocurrency from the weak attackers using a hidden trap. In this paper, we propose a machine-learning model that can detect such honeypots with high performance and prevent theft before it occurs. We use a term-frequency inverse document-frequency method to extract feature words and word2vec to learn distributed representations for the Solidity bytecode. As a result, we achieved higher PR-AUC scores in honeypot detection compared with previous efforts. Based on this, we demonstrate that the smart contract code contains useful information for honeypot detection. Furthermore, our proposed method works without using features that become available after theft. Hence, the method enables us to predict incidents and reduce the number of honeypot victims.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
With the evolution and proliferation of blockchain, the technology is becoming more prevalent in enterprise software development. Using the already proven DevOps approach in this setting makes sense, as it can accelerate the general pace of software development and delivery, improve software quality, and increase overall productivity. However, there is currently a lack of guidance on a structured DevOps approach and a breakdown of the specifics in the context of blockchain-based software development. Therefore, we combined gray literature and DevOps application studies from pertinent GitHub projects to systematically investigate current practices and solution approaches for an efficient blockchain-oriented DevOps procedure. In this process, we elaborated procedural steps and related activities according to the main stages of Continuous Integration and Continuous Delivery. Our research shows that core DevOps concepts and activities are similar to other areas and are entirely possible with already established CI/CD solutions that orchestrate the right tools, with the difference that more rigorous testing and differentiated deployment practices are required due to the inherent immutability of blockchain.
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.
Currently, the blockchain technology has been widely applied to various industries, and has attracted wide attention. However, because of its unique anonymity, digital currency has become a haven for all kinds of cyber crimes. It has been reported that Ethereum frauds provide huge profits, and pose a serious threat to the financial security of the Ethereum network. To create a desired financial environment, an effective method is urgently needed to automatically detect and identify Ethereum frauds in the governance of the Ethereum system. In view of this, this paper proposes a method for detecting Ethereum frauds by mining Ethereum-based transaction records. Specifically, web crawlers are used to capture labeled fraudulent addresses, and then a transaction network is reconstructed based on the public transaction book. Then, an amount-based network embedding algorithm is proposed to extract node features for identifying fraudulent transactions. At last, the graph convolutional network model is used to classify addresses into legal addresses and fraudulent addresses. The experimental results show that the system for detecting fraudulent transactions can achieve the accuracy of 95%, which reflects the excellent performance of the system for detecting Ethereum fraudulent transactions.
This paper aims to build the design of a secure and trustworthy platform of digital document sharing system, where students may share their officially obtained educational documents, e.g. Certificates or Academic Transcripts, with potential employers. The system was designed on top of the Ethereum Blockchain, coupled with IPFS as the document storage system. The design has been arranged in such a way as to work as a nationwide solution.
Zain Abubaker, Asad Ullah Khan, Ahmad Almogren, Shahid Abbas · 7 authors
Abstract In this article, Internet of Things (IoTs) devices are used for sensing the data through which the device owners earn revenue. Interested users can purchase data from IoT device owners, according to their demands. However, users are not confident about the quality of data they are purchasing. Moreover, the users do not rely on the device owner and are not willing to initiate data trading. Currently, data trading systems have many drawbacks, as they involve a third party, security and reputation mechanisms. Therefore, in this article, IoTs and BlockChain (BC) are integrated to monetize IoT's data and provide trustful data trading. A BC based review system to monetize IoT's data trading is developed through Ethereum smart contracts. The review system encourages the owners to provide authentic data and solves the issues regarding data integrity, fake reviews and conflicts between entities. Reviews and ratings are stored in the BC database for providing a guarantee about the data quality to users. To maintain data integrity, we use an advanced encryption standard (AES)‐256 encryption technique to encrypt data. Moreover, an arbitrator entity is responsible to resolve conflicts between data owner and users. The incentive is provided to the users and arbitrators to increase user participation and honesty. Simulations are performed for the validation of our system. We examine the proposed model using three parameters: gas consumption, mining time and encryption time.
In real-world scenarios, in order to encourage one to report others crimes, judicial department usually rents independent cloud storage spaces to receive the precious evidences from whistleblowers. Since the uploaded data are not controlled by cloud users, remote data integrity is very important. Public cloud auditing enables an auditor to periodically check the integrity of outsourcing data on behalf of users, without retrieving the entire data file. However, most existing data auditing schemes have potential security vulnerabilities, and thus cannot defense many security attacks (e.g., the man-in-the-middle attack). Meanwhile, it is significant to protect whistleblower’s identity privacy, reward the real data uploader, and further trace the responsibility of slanders accurately. From the aforementioned requirements, we present an efficient blockchain-assisted conditional anonymity privacy-preserving public auditing (BA-CAPPPA) scheme with reward mechanism. The Ethereum blockchain is integrated into BA-CAPPPA to enhance the security level of the whole public auditing mechanism. Theoretical analysis results show that the BA-CAPPPA achieves man-in-the-middle attack resistance, storage correctness guarantee, data privacy-preservation, conditional identity anonymity, and reward mechanism. Performance evaluations and comparisons demonstrate that BA-CAPPPA could outperform some state-of-the-art data auditing schemes.
Purpose – This study attempts to establish if the markets for the two most popular cryptocurrencies in the world, Bitcoin and Ethereum, follow weak-form market efficiency across various landmarks in time. Design/Methodology/Approach – Traditional testing for establishing weak-form market efficiency rests on whether the price series exhibits a random walk process, which implies that future prices cannot be predicted. However, not all random walk series automatically imply weak-form market efficiency, since some asset price behaviors may exhibit non-constant variance. In such cases, the GARCH model can be used to test for the presence of market efficiency. Since structural breaks in the prices of both cryptocurrencies are common, tests for market efficiency were carried out using sub-temporal price windows. In both price series, the last time window coincided with the 2020 COVID-19 pandemic period. Findings – Results of the GARCH analyses showed that the volatility and persistence parameters (α and β, respectively) in the Bitcoin and Ethereum models were all statistically significant, implying that prices in their sub-temporal markets were generally weak-form inefficient. The observed market inefficiency in both cryptocurrencies can be attributed to various factors like the price manipulation of crypto whales, security issues, and increased media attention, which led to inflows of information that helped big investors beat and gain from the market by successfully predicting the trend in future prices. During the 2020 COVID-19 pandemic period, both cryptocurrencies’ prices were observed to rise significantly, similar to the case of the 2017 Bitcoin price bubble. A cointegrating regression between Bitcoin and Ethereum prices during this period, however, showed a spurious relationship. Despite the absence of a long run relationship between these two price series, the current price bubbles in the cryptocurrency markets are speculated to be tied together. Research Implications – Players in the cryptocurrency market must always be cautious in making investment decisions regarding this type of asset since the markets are generally price inefficient and risky; any idiosyncratic decision that may be triggered by a price bubble burst in one cryptocurrency market may or may not serve as a signal that the other market will do the same. Since the Bitcoin and Ethereum prices were shown to exhibit volatility spillover and persistence, investors can use this information to make informed decisions as to whether to invest in these cryptocurrencies despite the huge risks that are magnified during the COVID-19 pandemic.
The Proof of Stake algorithm replaces miners with validators and makes the mining process virtual. Ethereum developers have always wanted to switch to Proof of Stake, but it presents some problems, on which developers are working.
I. S. Ivanchenko, Marina V. Charaeva, Alla A. Lysochenko, Ilya A. Nozhenkov
Since 2009, cryptocurrencies being a modern form of electronic means of payment have become widespread in the global financial market. In this regard, a study aimed to find an answer to the question: “Are cryptocurrencies a modern form of money?” was conducted. An analysis of the scientific works of leading economic schools has led to the conclusion that cryptocurrencies are a modern form of private money that performs the main monetary function being a means of payment, which corresponds to the idea of the Austrian economic school of full-fledged means of payment. The study attempts to predict the market rate of the three most popular cryptocurrencies at present being Bitcoin, Ethereum and Ripple due to the fact that modern cryptocurrencies demonstrate a high level of volatility in their market value, and reliable funds must maintain their purchasing power. The analysis of the cryptocurrency market with regard to the information efficiency has led to the conclusion that cryptocurrencies have been demonstrating instability of qualitative properties over the past five years. The authors proposed to improve the predictive characteristics of the HAR-RV model by additionally calculating the Shannon information entropy of the initial time series to level their insensitivity to unexpected information shocks in the cryptocurrency market being the main drawback of regression models. The study has proved that cryptocurrencies are a promising modern form of electronic money, their market rate is quite predictable, and the popularity of cryptocurrencies and their use in payment transactions will further increase.
Vikram Kanth, Chad Bollmann, Murali Tummala, John McEachen
As digital trust has declined, services purporting to provide privacy and anonymity have become increasingly popular in today's online environment. While there are several examples of these types of applications, blockchain-based services like Bitcoin and Ethereum have emerged as a potential answer to some of these privacy concerns. Unfortunately, many of the same features that facilitate that privacy and anonymity can also be leveraged by nefarious actors to transmit and store information covertly. These features can also be used by government and military organizations for communications purposes. In this paper, we present a generic information hiding model incorporating anonymity that builds on existing classical steganographic models like the Prisoners' Problem. We then analyze our model with regards to blockchain protocols and present a novel blockchain-based address embedding scheme. Finally, we implement our scheme using the Ethereum platform.
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Recently, phishing scams have posed a significant threat to blockchains. Phishing detectors direct their efforts in hunting phishing addresses. Most of the detectors extract target addresses’ transaction behavior features by random walking or constructing static subgraphs. The random walking methods, unfortunately, usually miss structural information due to limited sampling sequence length, while the static subgraph methods tend to ignore temporal features lying in the evolving transaction behaviors. More importantly, their performance undergoes severe degradation when the malicious users intentionally hide phishing behaviors. To address these challenges, we propose TEGDetector, a dynamic graph classifier that learns the evolving behavior features from transaction evolution graphs (TEGs). First, we cast the transaction series into multiple time slices, capturing the target address’s transaction behaviors in different periods. Then, we provide a fast nonparametric phishing detector (FD) to narrow down the search space of suspicious addresses. Finally, TEGDetector considers both the spatial and temporal evolutions toward a complete characterization of the evolving transaction behaviors. Moreover, TEGDetector utilizes adaptively learned time coefficient to pay distinct attention to different periods, which provides several novel insights. Extensive experiments on the large-scale Ethereum transaction dataset demonstrate that the proposed method achieves state-of-the-art (SOTA) detection performance. The code of TEGDetector is open sourced at https://github.com/Seaocn/TEGDetector.
This work presents how a digital identity management system can support food supply chains in guaranteeing the quality of the products marketed and the compliance of the several supply-chain’s nodes to standards and technical regulations. Specific goal of this work is to present a system that provides full visibility of process/food certifications, which nowadays are issued by accredited and approved certification bodies (issuers) and delivered and stored in paper version by the several participants (holders) of the supply chain. The system is designed and implemented by combining the latest most innovative and disruptive technologies in the market—Self Sovereign Identity system, Blockchain, and Inter Planetary File System. The crucial aspects that it aims to hit are the storage and access of food/process certifications, and the proper eligibility verification of these certifications exploiting the concepts of the Self Sovereign Identity-based models. The proposed system, realized by using standards that are WWW Consortium-compatible and the Ethereum Blockchain, ensures eligibility, transparency, and traceability of the certifications along a food supply chain, and could be an innovation model/idea that the companies that adopt the Open Innovation paradigm might want to pursue.
Mahendra Swain, Dilip Lilaramani, G. Mahesh, A K Srivastva
Increasing demand of renewable energy is driving the energy producers towards secured and decentralized network for energy trading. A decentralized framework could mitigate security threats in smart grid. Blockchain based smart contracts could improve the need of interaction producer and consumer by implementing reliable distributed transactions. This help to reduce interaction of third parties which causes security vulnerability. A decentralized infrastructure using Ethereum is proposed in this study. Ethereum keeps the track of transactions taken place from the consumers to the supply industries using crypto algorithms. Creating private node using Ethereum framework facilities the decentralization, anonymity, transparency and secure in the network. A private node using RISC-V platform is demonstrated in this paper. Smart contracts for the energy trading is written on solidity remix simulator and further validated on U540 RISC-V platform. End to end energy trading in smart grid using private Ethereum blockchain is demonstrated. Performance of the transaction, encryption and reliability of the application is validated over the platform.
Vehicular Digital Forensics (VDF) is essential to enable liability cognizance of accidents and fight against crimes. Ensuring the authority to timely gather, analyze, and trace data promotes vehicular investigations. However, adversaries crave the identity of the data provider/user, damage the evidence, violate evidence jurisdiction, and leak evidence. Therefore, protecting privacy and evidence accountability while guaranteeing access control and traceability in VDF is no easy task. To address the above-mentioned issues, we propose Eunomia: an anonymous and secure VDF scheme based on blockchain. It preserves privacy with decentralized anonymous credentials without trusted third parties. Vehicular data and evidence are uploaded by data providers to the blockchain and stored in distributed data storage. Each investigation is modeled as a finite state machine with state transitions being executed by smart contracts. Eunomia achieves fine-grained evidence access control via ciphertext-policy attribute-based encryption and Bulletproofs. A user must hold specific attributes and a temporary-and-unexpired token/warrant to retrieve data from the blockchain. Finally, a secret key is embedded into data to trace the traitor if any evidence breach happens. We use a formal analysis to demonstrate the strong privacy and security properties of Eunomia. Moreover, we build a prototype in a WiFi-based Ethereum test network to evaluate its performance.