Ethereum has adopted a rollup-centric roadmap to scale by making rollups (layer 2 scaling solutions) the primary method for handling transactions. The first significant step towards this goal was EIP-4844, which introduced blob transactions that are designed to meet the data availability needs of layer 2 protocols. This work constitutes the first rigorous and comprehensive empirical analysis of transaction- and mempool-level data since the institution of blobs on Ethereum on March 13, 2024. We perform a longitudinal study of the early days of the blob fee market analyzing the landscape and the behaviors of its participants. We identify and measure the inefficiencies arising out of suboptimal block packing, showing that at times it has resulted in up to 70% relative fee loss. We hone in and give further insight into two (congested) peak demand periods for blobs. Finally, we document a market design issue relating to subset bidding due to the inflexibility of the transaction structure on packing data as blobs and suggest possible ways to fix it. The latter market structure issue also applies more generally for any discrete objects included within transactions.
The Ethereum blockchain operates as a decentralized platform, utilizing blockchain technology to distribute smart contracts across a global network. It enables currency and digital value exchange without centralized control. However, the exponential growth of online commerce has created a fertile ground for a surge in fraudulent activities such as money laundering and phishing, thereby exacerbating significant security vulnerabilities. To combat this, our article introduces an ensemble learning approach to accurately detect fraudulent Ethereum blockchain transactions. Our goal is to integrate a decision-making tool into the decentralized validation process of Ethereum, allowing blockchain miners to identify and flag fraudulent transactions. Additionally, our system can assist governmental organizations in overseeing the blockchain network and identifying fraudulent activities. Our framework incorporates various data pre-processing techniques and evaluates multiple machine learning algorithms, including logistic regression, Isolation Forest, support vector machine, Random Forest, XGBoost, and recurrent neural network. These models are fine-tuned using grid search to enhance their performance. The proposed approach utilizes an ensemble of three distinct models (Random Forest, extreme gradient boosting (XGBoost), and support vector machine) to further improve classification performance. It achieves high scores of over 98% across key classification metrics like accuracy, precision, recall, and F1-score. Moreover, the approach is suitable for real-world usage, with an inference time of 0.13 s.
Bahar Acilan, Andrei Constantinescu, Lioba Heimbach, Roger Wattenhofer
Given the low throughput of blockchains like Bitcoin and Ethereum, scalability - the ability to process an increasing number of transactions - has become a central focus of blockchain research. One promising approach is the parallelization of transaction execution across multiple threads. However, achieving efficient parallelization requires a redesign of the incentive structure within the fee market. Currently, the fee market does not differentiate between transactions that access multiple high-demand storage keys (i.e., unique identifiers for individual data entries) versus a single low-demand one, as long as they require the same computational effort. Addressing this discrepancy is crucial for enabling more effective parallel execution. In this work, we aim to bridge the gap between the current fee market and the need for parallel execution by exploring alternative fee market designs. To this end, we propose a framework consisting of two key components: a Gas Computation Mechanism (GCM), which quantifies the load a transaction places on the network in terms of parallelization and computation, measured in units of gas, and a Transaction Fee Mechanism (TFM), which assigns a price to each unit of gas. We additionally introduce a set of desirable properties for a GCM, propose several candidate mechanisms, and evaluate them against these criteria. Our analysis highlights two strong candidates: the weighted area GCM, which integrates smoothly with existing TFMs such as EIP-1559 and satisfies a broad subset of the outlined properties, and the time-proportional makespan GCM, which assigns gas costs based on the context of the entire block's schedule and, through this dependence on the overall execution outcome, captures the dynamics of parallel execution more accurately.
While blockchain technology is viewed to revolutionize the energy sector by its cryptography-based, open, and direct peer-to-peer energy trading (P2PET) from producer to consumer, the current paper focused on the blockchain framework developed that allows for P2PET in the retail electricity market. The platform makes sure that there is proper supply-demand matching, transaction streamlining, and increased need for direct interaction, hence reducing the need for brokers on the platform. Its design monitors the entire energy trading process, with smart contracts automating payments and transactions to ensure security and fairness. Tests in a private Ethereum environment demonstrate benefits like accurate market pricing, fair profit distribution, and better renewable energy integration. It also incentivizes the participation of stakeholders in the P2PET through high-value information on gas usage, introducing computational efficiency. Besides, this proposed model adopted a consensus mechanism that would guarantee the permanence, scalability, and robustness of transactions across ...
ABSTRACT This paper investigates the current landscape of option trading platforms for cryptocurrencies, encompassing both centralized and decentralized exchanges. Option contracts in cryptocurrency markets offer functionalities akin to traditional markets, providing investors with tools to mitigate risks, particularly those arising from price volatility, while also allowing them to capitalize on future volatility trends. The paper discusses these applications of option contracts in the context of decentralized finance (DeFi), emphasizing their utility in managing market uncertainties. Despite a recent surge in the trading volume of options contracts on cryptocurrencies, decentralized platforms account for less than 1 % of this total volume. Hence, this paper takes a closer look by examining the design choices of these platforms to understand the challenges hindering their growth and adoption. It identifies technical, financial, and adoption‐related challenges that decentralized exchanges face and provides commentary on existing platform responses. Subsequently, the paper analyzes the impact of absent options markets on the inefficiencies of automated market maker liquidity. It examines historical on‐chain data for 14 ERC20 token pairs on Ethereum. The analysis shows 1143 instances in which deeper liquidity levels, as high as more, could have been achieved by establishing an options market.
Smart contract as a new form of contract is recognized to provide speedy and efficient transactions. Eliminating textual ambiguities, cumbersome contractual terms, enables negotiations, verify terms and automatically enforce tempered-free contractual terms without the need for intermediaries. In traditional contract, contractual terms are written in formal language which are quite cumbersome, the process of concluding transactions is slow and requires the intervention of lawyers, banks, registry departments and the courts. Many jurisdictions including Nigeria are still carrying out contractual transactions relying solely on traditional contract despite advancement in technologies including Blockchain technology, Ethereum and use of cryptocurrencies like Bitcoin and others as medium of exchange in online transactions. Using a qualitative doctrinal legal research method, this research gathered online sources and examined the legality of smart contract and Blockchain technology. While highlighting the importance of using smart contract in business transaction in Nigeria. The research adds to ongoing discuss on smart contracts and Blockchain technology. Suggesting the need for a shift from purely traditional contracts in Nigeria, to adoption of smart contracts to ease both domestic and trans-jurisdictional transactions mostly concluded online. However, there is a need for a robust framework for smart contract in Nigeria just like the E-SIGN Act and the UETA in the United States and other similar legislations that have been developed in other countries across the world.
Muhamad Abdul Fatah, Martanto Martanto, Arif Rinaldi Dikananda, Ahmad Rifa’i
Predicting erratic assets like Ethereum is difficult in the dynamic cryptocurrency market. This study uses an enhanced Support Vector Regression (SVR) algorithm to create a daily price prediction model for Ethereum. Yahoo Finance provided the data, which was preprocessed to include missing value cleaning, normalization, and feature extraction of Moving Average (MA) and Exponential Moving Average (EMA). The data was collected between August 4, 2019 and August 4, 2024. An ideal combination was obtained by parameter optimization with GridSearchCV: gamma scale, linear kernel, epsilon of 1, and C of 100. The model performed well, as evidenced by its R2 of 0.9985 and MSE of 2137.97. The model's reliability in predicting Ethereum's price movement patterns was validated via prediction graphs. A 30-day forecast indicated a stable trend, with prices slightly decreasing from $2921.31 on January 1, 2025, to $2919.83 on January 31, 2025. These results highlight the importance of data preprocessing and parameter optimization in enhancing SVR model performance.
Many blockchains such as Ethereum execute all incoming transactions sequentially significantly limiting the potential throughput. A common approach to scale execution is parallel execution engines that fully utilize modern multi-core architectures. Parallel execution is then either done optimistically, by executing transactions in parallel and detecting conflicts on the fly, or guided, by requiring exhaustive client transaction hints and scheduling transactions accordingly. However, recent studies have shown that the performance of parallel execution engines depends on the nature of the underlying workload. In fact, in some cases, only a 60% speed-up compared to sequential execution could be obtained. This is the case, as transactions that access the same resources must be executed sequentially. For example, if 10% of the transactions in a block access the same resource, the execution cannot meaningfully scale beyond 10 cores. Therefore, a single popular application can bottleneck the execution and limit the potential throughput. In this paper, we introduce Anthemius, a block construction algorithm that optimizes parallel transaction execution throughput. We evaluate Anthemius exhaustively under a range of workloads, and show that Anthemius enables the underlying parallel execution engine to process over twice as many transactions.
Anber Abraheem Shlash Mohammad, Ammar Mohammad Al-Ramadan, Suleiman Ibrahim Mohammad, Badrea Al Oraini · 8 authors
Sustainability in food supply chains is a critical global challenge, particularly in resource-constrained regions like Jordan, where operational inefficiencies and environmental concerns are prevalent. This study explores the integration of blockchain and artificial intelligence (AI) technologies to enhance metadata management, forecast sustainability metrics, and support decision-making in Jordan’s food supply chains. Blockchain's ability to improve metadata accuracy, standardization, and traceability, combined with AI’s predictive capabilities, offers a powerful solution for addressing sustainability challenges.MethodsThe research employed a mixed-methods approach, combining real-time data from blockchain transaction logs, AI-generated forecasts, and stakeholder surveys. Blockchain data from platforms like Hyperledger Fabric and Ethereum provided insights into metadata accuracy and traceability. AI models were developed using machine learning techniques, such as linear regression, to forecast food waste reduction, carbon footprint reduction, and energy efficiency. Multi-Criteria Decision Analysis (MCDA), using AHP and TOPSIS, was applied to evaluate trade-offs among sustainability goals.ResultsThe results revealed significant improvements in metadata accuracy (from 83% to 96.66%) and reductions in traceability time (from 4.0 to 2.35 hours) following blockchain implementation. AI models demonstrated high predictive accuracy, explaining 88%, 81%, and 76% of the variance in food waste reduction, carbon footprint reduction, and energy efficiency, respectively. ConclusionThis study underscores the transformative potential of blockchain and AI technologies in achieving sustainability goals. By fostering transparency, predictive insights, and data-driven decision-making, these innovations can address key challenges in Jordan’s food supply chains, offering actionable strategies for stakeholders.
Moch Panji Agung Saputra, Riza Andrian Ibrahim, Renda Sandi Saputra
The increasing use of cryptocurrencies has changed the dynamics of investment, presenting both opportunities and challenges for investors. Although various studies have compared the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting financial asset prices, there are still differences in results regarding which model is superior. Therefore, this study aims to compare the performance of LSTM and GRU in predicting Ethereum prices using a hyperparameter tuning approach. The data used is historical data of Ethereum (ETH) shares from 2020 to 2025. The research methodology includes data preprocessing using Min-Max scaling, model development with various layer configurations, and comprehensive evaluation using several performance metrics. The results show that the GRU Model provides superior performance with a lower Root Mean Squared Error (RMSE) of 0.0234 and Mean Absolute Error (MAE) of 0.0168, compared to LSTM's RMSE of 0.0265 and MAE of 0.0193. While LSTM exhibits a slightly better Mean Absolute Percentage Error (MAPE) of 18.08% compared to GRU at 18.17%, the GRU model achieves a higher R² Score of 0.9442 compared to LSTM at 0.9282. Visual analysis of the prediction patterns and residual distributions further demonstrates GRU’s more consistent and accurate performance in capturing Ethereum price movements. These findings suggest that while both models are effective for cryptocurrency price prediction, GRU offers slightly better overall performance and stability, especially in maintaining consistent prediction accuracy across different market conditions.
Ethereum has been a cornerstone of the decentralized ecosystem, with rollup-based scaling solutions like Arbitrum and Optimism significantly expanding its capabilities. These rollups enhance scalability and foster innovation, but their rapid proliferation has introduced \emph{liquidity fragmentation}. Specifically, tokens distributed on multiple rollups fragment the liquidity of users, complicating participation in trading and lending activities bound by minimum liquidity thresholds. This paper proposes UAT20, a universal abstract token standard, to address liquidity fragmentation across rollups. Leveraging Conflict-free Replicated Data Types (CRDTs), UAT20 ensures consistent states across multiple rollups. We introduce a two-phase commit protocol to resolve transaction conflicts, enabling seamless and secure liquidity unification. Finally, our empirical analysis demonstrated the necessity and effectiveness of UAT20 in mitigating liquidity fragmentation within Rollups.
Feng Chen, Chunjiang Zhao, Xinting Yang, Na Luo · 5 authors
Agri-food safety issues have received widespread attention globally. The emergence of blockchain technology (BCT) effectively addresses trust issues in the agri-food supply chain traceability system (AFSCTS). However, the append-only feature of blockchain has led to continuous linear data growth in BCT-based AFSCTSs, which increases the equipment requirements and has become a bottleneck for BCT-based AFSCTS applications. The storage capacity required by BCT-based AFSCTSs can be effectively reduced by deleting expired data, thereby reducing the storage pressure on blockchain devices and lowering the device requirements. In this paper, we propose an AFSCTS architecture that incorporates redactable blockchain and InterPlanetary file system (IPFS) technologies to achieve traceability with low storage pressure, using the wheat supply chain as a proof of concept. Firstly, the key links were analyzed in agri-food traceability and the demand was proposed for agri-food blockchain traceability based on the timeliness of traceability data. Secondly, a lightweight accountable parallel blockchain architecture called LAP-chain is proposed. This architecture utilizes redactable blockchain technology to offload expired agri-food traceability data to IPFS, thereby reducing the storage pressure on blockchain devices and ensuring data accountability through IPFS. Finally, we evaluate the correctness, collision resistance, and storage performance of the LAP-chain built on the Ethereum private chain. The results show that when expired agri-food traceability data are permanently retained, the storage capacity of the proposed architecture is only 52.38% of that of the traditional blockchain traceability architecture, after running continuously for 36 months. When traceability data of expired agri-food are deleted in accordance with the food laws and regulations of various countries, the storage capacity of the proposed architecture can be reduced from a linear level to a constant level compared to the traditional blockchain traceability architecture. The proposed architecture has the potential to contribute to improving the safety and quality of agri-food.
The proliferation of counterfeit academic certificates has engendered unethical practices, thereby depriving meritorious candidates of potential opportunities; this situation subsequently renders conventional document ver- ification methodologies ineffective due to their inherent time-consuming nature, high costs, and susceptibility to manipulation. In response to these pressing challenges, this paper advocates for a blockchain-based decentralized document verification framework that leverages the InterPlanetary File System (IPFS) and Ethereum blockchain, thereby enhancing security, transparency, and operational efficiency. The system adheres to a meticulously struc- tured methodology whereby applicants initially submit their credentials, which are subsequently authenticated by educational institutions prior to their storage within the IPFS for decentralized file management; concurrently, only the hash of these credentials is retained on the blockchain, effectively reducing costs and augmenting scalability. To assess the system’s efficacy, it was subjected to rigorous testing employing multiple concurrent mechanisms, including Proof of Work, Proof of Stake, and Practical Byzantine Fault Tolerance, with findings demonstrating that Proof of Stake offers the most optimal equilibrium between velocity and security. The prototype that was developed showcased significant improvements, with verification accuracy, reduced the processing time also minimized the manual effort, furthermore, making the process significantly more efficient than traditional methods. Furthermore, the system accomplished a transaction throughput of 1000 transactions per second, accompanied by an average confirmation time of 5 seconds, thus significantly enhancing operational efficiency for employers and institutions engaged in credential verification. Additionally, a thorough comparative analysis was conducted against traditional methodologies concerning security, processing velocity, and cost-effectiveness, ensuring validation through tamper- proof mechanisms and the mitigation of fraud risks associated with certificates. This research not only bolsters the reliability of document verification but also lays the groundwork for future advancements, including cross-chain integration, AI-driven fraud detection systems, and mobile-based verification applications, all of which aspire to further optimize efficiency and accessibility within academic credential verification processes.
Ajo Dian Yusandika, Alamgir Hossain Bhuiyan, Partho Sarothi Laskar
The rapid expansion of decentralized exchanges (DEXs) has reshaped cryptocurrency trading, yet comparative cross-chain evaluations remain scarce, particularly in transaction efficiency, liquidity sustainability, and economic viability. This study conducts an on-chain comparative analysis of Uniswap (Ethereum), Raydium (Solana), and PancakeSwap (BSC) by examining transaction count, trade volume, protocol revenue, and total value locked (TVL). Using real-time blockchain data from DefiLlama, Dune Analytics, Artemis, Token Terminal, and Arkham, this research applies quantitative cross-chain modeling to assess the performance of each ecosystem. Findings reveal that Ethereum leads in trade volume and liquidity depth, driven by institutional adoption despite high transaction costs. Solana exhibits superior transaction efficiency, attracting high-frequency traders, though its lower TVL suggests liquidity retention challenges. BSC, once a major DeFi player, now faces stagnation, with declining trade volume and fragmented liquidity. Unlike prior studies focusing solely on transaction metrics, this research integrates protocol revenue as a sustainability indicator, offering a broader understanding of DEX viability. The results underscore the need for cross-chain liquidity bridges and Layer-2 scaling solutions to mitigate liquidity fragmentation and enhance DeFi efficiency. This study contributes to both academia and industry by providing empirical insights for blockchain developers, investors, and policymakers, emphasizing the importance of scalability, liquidity incentives, and revenue optimization. The findings support future research on multi-chain liquidity integration, AI-driven market-making, and governance frameworks, guiding the next evolution of decentralized financial ecosystems.
We present the design and implementation of a fourth-year undergraduate computer science elective course on cryptocurrency. The course introduces the basics of cryptocurrencies and blockchains using Bitcoin. Programming distributed applications for blockchains is explored using Ethereum via the Solidity programming language. Advanced theoretical concepts are discussed, using different cryptocurrencies to demonstrate how they are implemented in practice. Cryptocurrency applications are presented, along with a responsible examination of present-day cryptocurrency scams. The assignments build upon each other, with the students implementing a web-based blockchain application by the end of the semester. The course was well received, and student feedback was positive. All of the course materials are available online under public copyright licenses.
Future generations of wireless networks at high-frequency spectrum suffer from limited coverage and Non-Line- of-Sight signal blockage, challenging emerging applications, such as smart industries and intelligent automation systems. Collaborative and cooperative communications with smart relays via Non-Orthogonal Multiple Access (NOMA) could be a breakthrough solution to this challenge. This paper presents a blockchain-integrated framework for NOMA wireless communication systems that incentivizes cooperation among users serving as relays. By leveraging Ethereum-based smart contracts, we introduce a Service Verification Contract featuring a Proof of Quality of Experience (PQoE) mechanism. The contract uses trust scores, weighted verifications, and dynamic validation thresholds to ensure honest behavior and deter malicious activities. The simulation results show that honest participants gradually increase their trust scores and require fewer verifications, while malicious verifiers lose influence over repeated rounds. Our findings indicate that combining trust-based incentives with a decentralized ledger can effectively promote reliable data-relaying services and streamline payment processes in collaborative and smart wireless networking systems.
Cheques are a means of payment. It is a vehicle for so-called scriptural money. Because of its cashless nature, cheques are subject to shortcomings in terms of both security and ease of use. This article presents a system for dematerialising cheques, facilitating their certification and tracking through the use of revolutionary web3 technologies such as blockchain, interplanetary file systems and decentralised applications. The system relies on the Ethereum blockchain, whose Proof-of-Work (PoW) consensus mechanism guarantees the security and integrity of transactions through decentralized block validation. Until now, issuing a cheque has required the physical contribution and presence of both the drawer and the bearer. We enable the drawer to digitally issue and manage a cheque, as well as the possibility of designating the bearer or not. For the account manager, the possibility of better satisfying the customer's needs by optimising processing time and automating tasks that do not require his or her involvement. The system also optimises processing time and eliminates the risks associated with physical cheques, offering a more secure and efficient alternative. All these operations, of course, take place in a secure environment, a security offered by the blockchain with its use of cryptography and decentralised file system.
To govern smart contracts running on Ethereum, multiple Ethereum Request for Comment (ERC) standards have been developed, each defining a set of rules governing contract behavior. Violating these rules can cause serious security issues and financial losses, signifying the importance of verifying ERC compliance. Today's practices of such verification include manual audits, expert-developed program-analysis tools, and large language models (LLMs), all of which remain ineffective at detecting ERC rule violations. This paper introduces SymGPT, a tool that combines LLMs with symbolic execution to automatically verify smart contracts' compliance with ERC rules. We begin by empirically analyzing 132 ERC rules from three major ERC standards, examining their content, security implications, and natural language descriptions. Based on this study, SymGPT instructs an LLM to translate ERC rules into a domain-specific language, synthesizes constraints from the translated rules to model potential rule violations, and performs symbolic execution for violation detection. Our evaluation shows that SymGPT identifies 5,783 ERC rule violations in 4,000 real-world contracts, including 1,375 violations with clear attack paths for financial theft. Furthermore, SymGPT outperforms six automated techniques and a security-expert auditing service, underscoring its superiority over current smart contract analysis methods.
This study is the first to scientifically investigate stock indices and currency exchanges that affect crypto price volatility pre and post the FTX (Future Exchanges) collapse event. Weekly series from 1 January 2020 to 31 December 2024 were utilized for the analysis. The ARDL model suggests positive symmetric short- and long-term effects of USA stock indices on Bitcoin and Ethereum prices (p < 0.10), while Japanese stock indices and currency exchanges have negative symmetric short- and long-term effects on Bitcoin and Ethereum price volatility (p < 0.10). The global index MSCI has no symmetric effect. The asymmetric approach NARDL suggests positive and negative asymmetric short- and long-term effects of USA and Japanese stock indices and currency exchanges on Bitcoin and Ethereum price volatility (p < 0.05). This research helps exchange brokers and crypto traders diversify their holdings, reduce stock index and currency exchange risk, and accurately predict Bitcoin and Ethereum price variations.
Hani Alnami, Muhammad Mohzary, Basem Assiri, Hussein Zangoti
The accurate prediction of cryptocurrency prices is crucial due to the volatility and complexity of digital asset markets, which pose significant challenges to traders, investors, and researchers. This research addresses these challenges by leveraging machine learning and deep learning techniques to forecast closing prices for cryptocurrencies, focusing on Bitcoin, Ethereum, Binance Coin, and Litecoin cryptocurrency datasets. A Random Forest ensemble learning algorithm, a Gradient Boosting model, and a feedforward neural network were implemented to handle the complexities in cryptocurrency data. A Z-Score-based anomaly detection framework was integrated to classify closing prices as normal or abnormal, aiding in identifying significant market events. Evaluation metrics, such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2), demonstrate the superior precision and reliability of the Random Forest and Gradient Boosting models. The deep learning model indicates strong generalization capabilities, suggesting potential advantages on more complex datasets. These findings highlight the importance of combining advanced machine learning techniques and cryptocurrencies to develop a robust framework for cryptocurrency forecasting and anomaly detection.
Blockchain innovation, best embodied by Ethereum, has revolutionized online transactions by making them more transparent and secure. However, the demand for more sophisticated fraudulent schemes increases with wider adoption, calling for more sophisticated fraud detection methods. Therefore, this paper contributes to the area of blockchain security by providing insights to regulators and stakeholders in Ethereum through an analysis of the Machine Learning (ML) models. We compare traditional approaches like logistic regression and decision trees with more advanced techniques like neural networks and ensemble methods. The performance of the model is measured using accuracy, precision, recall, and the ROC curve. The best accuracy of 0.98 is achieved by the optimized XGBoost framework.
We analyze developer activity across 10 major Ethereum repositories (totaling 129884 commits, 40550 issues) spanning 10 years to examine how events such as technical upgrades, market events, and community decisions impact development. Through statistical, survival, and network analyses, we find that technical events prompt increased activity before the event, followed by reduced commit rates afterwards, whereas market events lead to more reactive development. Core infrastructure repositories like Go-Ethereum exhibit faster issue resolution compared to developer tools, and technical events enhance core team collaboration. Our findings show how different types of events shape development dynamics, offering insights for project managers and developers in maintaining development momentum through major transitions. This work contributes to understanding the resilience of development communities and their adaptation to ecosystem changes.
Tato práce se zabývá rozšířením nástroje Tools for Solidity, rozšířením pro Visual Studio Code určeného pro vývoj smart kontraktů na platformě Ethereum. Práce analyzuje současný ekosystém vývoje na Ethereu se zaměřením na rozšíření Visual Studio Code a mezeru mezi interaktivními funkcemi Remix IDE a alternativami v rámci ekosystému. Po analýze praktická část práce detailně popisuje návrh produktu a implementaci funkcí pro nasazení na lokální blockchain a interaktivní testování kontraktů. Práce dále zahrnuje uživatelské testování a následná vylepšení založená na získané zpětné vazbě. Závěrečné kapitoly představují dodatečné funkce přidané nad rámec základní implementace a popisují možnosti budoucího vývoje.
Boao Zhang, Ningyu He, Xiaohui Hu, Kai Ma · 5 authors
Price manipulation attack is one of the notorious threats in decentralized finance (DeFi) applications, which allows attackers to exchange tokens at an extensively deviated price from the market. Existing efforts usually rely on reactive methods to identify such kind of attacks after they have happened, e.g., detecting attack transactions in the post-attack stage, which cannot mitigate or prevent price manipulation attacks timely. From the perspective of attackers, they usually need to deploy attack contracts in the pre-attack stage. Thus, if we can identify these attack contracts in a proactive manner, we can raise alarms and mitigate the threats. With the core idea in mind, in this work, we shift our attention from the victims to the attackers. Specifically, we propose SMARTCAT, a novel approach for identifying price manipulation attacks in the pre-attack stage proactively. For generality, it conducts analysis on bytecode and does not require any source code and transaction data. For accuracy, it depicts the control- and data-flow dependency relationships among function calls into a token flow graph. For scalability, it filters out those suspicious paths, in which it conducts inter-contract analysis as necessary. To this end, SMARTCAT can pinpoint attacks in real time once they have been deployed on a chain. The evaluation results illustrate that SMARTCAT significantly outperforms existing baselines with 91.6% recall and ~100% precision. Moreover, SMARTCAT also uncovers 616 attack contracts in-the-wild, accounting for \$9.25M financial losses, with only 19 cases publicly reported. By applying SMARTCAT as a real-time detector in Ethereum and Binance Smart Chain, it has raised 14 alarms 99 seconds after the corresponding deployment on average. These attacks have already led to $641K financial losses, and seven of them are still waiting for their ripe time.