Krzysztof Gogol, Johnnatan Messias, Deborah Miori, Claudio J. Tessone ยท 5 authors
This study quantifies the potential non-atomic MEV on Layer-2 (L2) blockchains by measuring the arbitrage opportunities between cross-rollup and DEX-CEX. Over recent years, we observe a shift in trading activities from Ethereum to rollups, with swaps on rollups occurring 2-3 times more frequently, albeit with lower trade volumes. By analyzing the costs of swap on L2s and price discrepancies cross-rollup and DEX-CEX, we identify more than 500 000 unexplored arbitrage opportunities. In particular, we find that these opportunities persist, on average, for 10 to 20 blocks, necessitating the modification of the Loss Versus Rebalancing (LVR) metric to prevent double-counting. Our findings indicate that the arbitrage opportunities in Arbitrum, Base, and Optimism range between 0.03% and 0.05% of the trading volume, while in the ZKsync it fluctuates around 0.25%.
Blockchain technology has emerged as a disruptive force in the realm of finance, offering decentralized and transparent mechanisms for conducting financial transactions. This paper explores the landscape of blockchain-based financial transactions, focusing on risk analysis, anomaly detection, regulatory frameworks, and ethical considerations. Drawing on interdisciplinary insights from finance, computer science, economics, law, and ethics, the study investigates the opportunities and challenges presented by blockchain finance. Leveraging quantitative analysis, machine learning algorithms, case studies, and regulatory reviews, the research sheds light on the complexities of blockchain ecosystems. Key findings include the importance of robust risk management strategies, the role of anomaly detection in safeguarding financial integrity, and the evolving regulatory landscape surrounding blockchain transactions. The study identifies gaps in current research and proposes avenues for future investigation, emphasizing the need for interdisciplinary approaches to address the multifaceted challenges of blockchain-based finance. Ultimately, this research aims to inform stakeholders about the implications of blockchain technology in financial transactions and foster responsible innovation and sustainable development in digital finance ecosystems.
Smart contracts are susceptible to various vulnerabilities that can lead to significant financial losses. The usage of tools for vulnerabilities is reducing the threats but presents some limitations related to the approach used by the tool itself. This paper presents a novel approach to smart contract vulnerability detection utilizing Large Language Models (LLMs), as a tool to detect all the vulnerabilities at once. Our proposed tool leverages the advanced natural language processing capabilities of LLMs to analyze smart contract code and identify potential security flaws. By training the LLM on a diverse dataset of known smart contract vulnerabilities and secure coding practices, we enhance its ability to recognize subtle and complex vulnerabilities that traditional static analysis tools might miss. The evaluation of our tool demonstrates its effectiveness in detecting a wide range of vulnerabilities with satisfaction and accuracy, providing developers with a robust mechanism to improve the security of their smart contracts before deployment. This approach signifies a significant advancement in the application of artificial intelligence for blockchain security, highlighting the potential of LLMs to enhance the reliability and safety of decentralized applications.
ROCYS is a knowledge resource for practitioners, scientists, and researchers working in various fields of cyber security, hacking, digital forensics, cyber warfare, viruses and worms domain or critical infrastructure protection area.
ROCYS is a knowledge resource for practitioners, scientists, and researchers working in various fields of cyber security, hacking, digital forensics, cyber warfare, viruses and worms domain or critical infrastructure protection area.
Smart contracts are programs that are deployed on blockchain to automate the agreements among users. It is hard to fix a security vulnerability once a smart contract is deployed. Vulnerabilities in smart contracts have raised a lot of concerns. Many automated solutions are proposed to identify these vulnerabilities. Supervised machine learning algorithms require historical data to detect vulnerabilities. These solutions will not work if the historical data for a vulnerability in question is not available. To address this problem, we have proposed to use the historical data of another vulnerability. We identify similar features among the vulnerabilities and use them to build the models. The built models are used to test smart contracts with the type of vulnerability that does not have the historical data. We have conducted experiments on 4 datasets and found that worst case $F_{1}$-score is 73% and the best case is 93% and mostly F1-score was more than 80%.
The proposed work investigates the use of lightweight convolutional neural networks (CNNs) for detecting vulnerability patterns in Solidity RGB-encoded smart contracts. Unlike heavy CNN models, which can be computationally intensive and fall short of optimal accuracy levels, the proposed study emphasizes efficiency. Transforming smart contract source code into RGB images not only reinforces security and protects proprietary information but also addresses compactness concerns, enabling convenient storage on online platforms. This approach ensures efficient use of bandwidth, enabling rapid scanning of contracts for potential vulnerabilities post-deployment. The streamlined mechanism allows for quick and simultaneous assessment of thousands of contracts within seconds, a task that proves challenging with rigorous formal verification tools. This methodology aligns with the need for both security and efficiency in the dynamic landscape of smart contract development and deployment.
In many smart contract architectures, every contract or object is mutably shared by default. The Sui smart contract platform bears the unique feature of distinguishing between shared and owned objects. While transactions operating on shared objects require consensus to sequence reads and writes, those involving only owned objects are independent and may bypass consensus; thus, the latter are less prone to this throughput bottleneck. However, it may not always be possible or desirable to avoid using shared objects. This article aims at identifying and investigating decentralized applications that require shared objects. Utilizing the Sui Rust SDK to query programmable transaction blocks, we analyze the frequency of transactions involving shared objects, shared resource contention levels, and most โpopularโ applications that contain shared objects. The presented results are reproducible and show the extensive usage of shared objects in Sui, low contention levels, and moderate dependency among shared objects in atomic transactions. This novel study of shared object use cases in a relatively new smart contract platform is important for improving the efficiency of such object-based architectures. This work is relevant for smart contract platform designers and smart contract developers.
This article deeply explores the applications and impacts of blockchain technology and smart contracts in the financial industry. First, it outlines the basic principles and characteristics of blockchain technology and smart contracts, and then analyzes their application examples in various financial fields such as cross-border payments, supply chain finance, equity financing and securities issuance, and the insurance industry. The study found that blockchain technology and smart contracts bring significant changes and opportunities to the financial industry by improving transparency, reducing transaction costs, and enhancing security. However, the application of these technologies also faces challenges such as technological maturity, legal and regulatory perfection, and data privacy protection. This article aims to provide valuable references for practitioners, researchers, and policy makers in the financial industry to promote the widespread application and healthy development of blockchain technology and smart contracts in the financial industry.
This paper introduces blockchain-based smart contracts and Decentralized Autonomous Organizations (DAOs) as compelling alternatives to conventional corporate structures. Coaseโs pioneering work in the 1930s posited that the decision to organize transactions within a firm hinges on whether it is more cost-effective than executing them in the open marketplace. However, if these transactions can be conducted more efficiently in the marketplace without the need for a traditional firm, that becomes the preferred approach. The advent of blockchain-based smart contracts, along with the adoption of numerous self-executing smart contracts, has the potential to significantly reduce the dependence on traditional firms. Coaseโs theorem was contingent on the magnitude of transaction costs, and if smart contracts can substantially diminish these costs, facilitating the emergence of DAOs, the original rationale for forming a firm may no longer apply when considering the blockchain paradigm.
In this work we explore ways to restrict the ability to call Solidity smart contract functions for a specified duration. We describe methods to restrict functions from being called twice in the same transaction, block, or time period. This is related to the notion of non-reentrant functions, which are functions that can be called within a previous execution. These methods can be used to restrict interactions with entire sets of functions of smart contracts. We are motivated to revisit this topic for two reasons. First, we note that sixteen real-world smart contracts exploits in 2023 resulting in over $136M USD lost or stolen that could have been prevented by restricting function calls. As part of this survey, we dissect a new class of exploit that involves so-called read-only reentrancy: exploits that re-enter read-only functions to use smart contract state inconsistencies in order to enable their exploitation. Second, while some of these approaches are simple, they may not always behave the same across different blockchains that support Solidity.
Vilhelm Sjรถberg, Kinnari Dave, Daniel Britten, Maria A. Schett ยท 9 authors
Programs executed on a blockchain - smart contracts - have high financial stakes; their correctness is crucial. We argue, that this correctness needs to be foundational: correctness needs to be based on the operational semantics of their execution environment. In this work we present a foundational system - the DeepSEA system - targeting the Ethereum blockchain as the largest smart contract platform. The DeepSEA system has a small but sufficiently rich programming language amenable for verification, the DeepSEA language, and a verified DeepSEA compiler. Together they enable true end-to-end verification for smart contracts. We demonstrate usability through two case studies: a realistic contract for Decentralized Finance and contract for crowdfunding.
Iskander Zulkarneev, Alexey Kirillovich Alexeev, Leonid Chernyatin
Smart contracts have become an integral part of blockchain networks worldwide. Due to the direct impact of smart contract security on the overall network security, it is crucial to prioritize this aspect. To accurately assess the security of smart contracts, a classification of vulnerabilities and a methodology for evaluating specific vulnerabilities are necessary. In the scientific community and audit communities throughout the existence of this technology, there hasnโt been a universally acknowledged classification or assessment methodology developed. This research analyzed existing methodologies and approaches for assessing the severity of vulnerabilities in both academic environments and practical bug bounty platform cases. The analysis identified shortcomings in these approaches, potential reasons for their appearance, and concluded on their ineffectiveness. The authors formulated criteria for a new classification of smart contract vulnerabilities, covering various fields and focused on practical applicability in the audit process and it is based on the architecture and life cycle of smart contracts.
Verifiable Delay Function (VDF) is a cryptographic concept that ensures a minimum delay before output through sequential processing, which is resistant to parallel computing. One of the significant VDF protocols academically reviewed is the VDF protocol proposed by Pietrzak. However, for the blockchain environment, the Pietrzak VDF has drawbacks including long proof size and recursive protocol computation. In this paper, we present an implementation study of Pietrzak VDF verification on Ethereum Virtual Machine (EVM). We found that the discussion in the Pietrzak's original paper can help a clear optimization in EVM where the costs of computation are predefined as the specific amounts of gas. In our results, the cost of VDF verification can be reduced from 4M to 2M gas, and the proof length can be generated under 8 KB with the 2048-bit RSA key length, which is much smaller than the previous expectation.
Dr.Karthick. M, N Anusri, Nithish kumar P, P Siva ยท 5 authors
Agriculture sector plays a pivotal role in the Indian economy, but farmers often face challenges like unpredictable weather, pest attacks, and erratic rainfall, leading to significant losses.Crop insurance is crucial to mitigate these risks and coverage for pre-sowing and post-harvest losses due to natural calamities.Block-chain based insurance system for farmers with smart contracts.The system is designed to provide farmers with insurance coverage against crop failure due to natural disasters.The system uses smart contract to automatically calculate and pay out insurance claims to farmers based on data from the weather cloud app.This approach involves using block-chain technology to create a decentralized and immutable ledger that records all insurance contracts, claims, and payouts.This system could greatly benefit farmers by providing them with more reliable and timely financial support in the event of adverse weather conditions or crop failures, thereby reducing their risk and encouraging greater investment in crop production.The crop index insurance is based on objectively measurable and verifiable data (e.g., weather data, satellite imagery) to determine insurance payouts, thus minimizing the need for on-the-ground assessments and reducing the risk of fraudulent claims.Furthermore, the model includes a decentralized application (DApp) interface for stakeholders to interact with the insurance policies seamlessly, view realtime data, and monitor the status of their coverage and claims.The utilization of Ethereum and Polygon networks ensures the scalability, sustainability, and accessibility of this solution, paving the way for a more resilient agricultural sector.
Massimo Bartoletti, Angelo Ferrando, E. Lipparini, Vadim Malvone
Smart contracts are an attractive target for attackers, as evidenced by a long history of security incidents. A current limitation of smart contract verification tools is that they are not really effective in expressing and verifying liquidity properties regarding the exchange of crypto-assets: for example, is it true that in every reachable state a user can fire a sequence of transactions to withdraw a given amount of crypto-assets? We propose Solvent, a tool aimed at verifying these kinds of properties, which are beyond the reach of existing verification tools for Solidity. We evaluate the effectiveness and performance of Solvent through a common benchmark of smart contracts.
Blockchain technology is a revolutionary concept that has transformed various industries, offering decentralized and secure solutions for data management. At its core, a blockchain is a distributed and immutable ledger that records transactions across a network of computers. One of the key features of blockchain is its transparency, as each participant in the network has access to the same information. The application of blockchain technology extends across diverse sectors, with finance being the most promising and well-known application domain. Cryptocurrencies, like Bitcoin and Ethereum, rely on blockchain to facilitate secure and transparent transactions. The decentralized nature of blockchain eliminates the need for intermediaries, reducing transaction costs and increasing efficiency. Beyond finance, blockchain is making significant strides in supply chain management. The emergence of blockchain technology has paved the way for a transformative innovation in the realm of digital transactions: smart contracts. These self-executing contracts encoded in blockchain have garnered significant attention for their potential to revolutionize the way agreements are made, executed, and enforced. This paper explores the concept of smart contracts, delving into the underlying technology, finding applications across various industries, legal implications, and future prospects.
Blockchain is a versatile technology with many potential applications, and one of them is in the field of real estate. Blockchain offers a high level of security and transparency, which are crucial in real estate transactions. While previous research mainly focuses on transactions between buyers and sellers, important elements like inspectorโs and lenderโs approval are often overlooked. This research introduces a groundbreaking project called Blockrealty, which addresses these limitations. The Blockrealty project specifically utilizes the Ethereum blockchain, leveraging its smart contract capabilities and widespread adoption for secure and transparent real estate transactions. Ethereumโs established infrastructure and community support make it an ideal platform for implementing the ERC-721 Smart Contract standard for real estate tokenization. It employs a unique three-step approval process involving buyers, lenders, inspectors, and sellers to ensure secure transactions, overcoming current challenges in the real estate industry. The project utilizes Non-Fungible Tokens (NFTs) for asset tokenization, using the ERC-721 Smart Contract standard to seamlessly convert real estate assets into digital tokens. To enhance data integrity and optimize hosting costs, the integration of the InterPlanetary File System (IPFS) ensures secure and decentralized storage for property images and metadata. Metamask integration provides a user-friendly interface for easy interaction. The research critically examines existing blockchain use cases in real estate, highlighting persistent problems that hinder the complete maturation of blockchain technology. The main aim is to create a digital real estate network using blockchain. This will help manage investment properties and make real estate contracts easier to distribute. It will make the real estate market more efficient and transparent.
Zhiyang Chen, Ye Liu, Sidi Mohamed Beillahi, Yi Li ยท 5 authors
Smart contract transactions associated with security attacks often exhibit distinct behavioral patterns compared with historical benign transactions before the attacking events. While many runtime monitoring and guarding mechanisms have been proposed to validate invariants and stop anomalous transactions on the fly, the empirical effectiveness of the invariants used remains largely unexplored. In this paper, we studied 23 prevalent invariants of 8 categories, which are either deployed in high-profile protocols or endorsed by leading auditing firms and security experts. Using these well-established invariants as templates, we developed a tool Trace2Inv which dynamically generates new invariants customized for a given contract based on its historical transaction data. We evaluated Trace2Inv on 42 smart contracts that fell victim to 27 distinct exploits on the Ethereum blockchain. Our findings reveal that the most effective invariant guard alone can successfully block 18 of the 27 identified exploits with minimal gas overhead. Our analysis also shows that most of the invariants remain effective even when the experienced attackers attempt to bypass them. Additionally, we studied the possibility of combining multiple invariant guards, resulting in blocking up to 23 of the 27 benchmark exploits and achieving false positive rates as low as 0.32%. Trace2Inv outperforms current state-of-the-art works on smart contract invariant mining and transaction attack detection in terms of both practicality and accuracy. Though Trace2Inv is not primarily designed for transaction attack detection, it surprisingly found two previously unreported exploit transactions, earlier than any reported exploit transactions against the same victim contracts.
Objective: By reviewing various previous works, this paper collects the multiple of approaches, strategies used to identify vulnerabilities in smart contracts. Blockchain is a decentralized technology that securely and immutably, records transactions across numerous computers in a visible manner. On a blockchain, smart contracts are self-executing agreements that independently execute and verify contract conditions. This reduces the need for middlemen and increases transparency. Smart contract vulnerabilities are problems in the code that could allow other parties to gain access to, alter, or steal assets as a result of mistakes, faults or imperfections made during development, thereby causing financial and operational harm. In this paper we have algorithms, techniques to detect vulnerabilities in smart contract using deep learning found in literature surveys. Methods: We have found some techniques using opcode, bytecode, Skip-Gram-Word2Vec to convert the smart contract file. Findings: We have found that LSTM, Vanilla-RNN, GRU have very less accuracy 49.64,53.68,54.54. Novelty & Applications: We will come with some different algorithms that will understand different vulnerability with more accuracy. We have come with CNN, Xception, EfficientNet-B2 which has accuracy high then LSTM, Vanilla-RNN, GRU i.e.71,69,75 percent.
The aim of this study is to examine whether the assets known as safe-haven assets during crises fulfill these qualities for equity investors in Turkey during the Covid-19 pandemic. According to the results obtained under the assumption of GJR-GARCH (1,1) error terms, no asset has shown safe-haven characteristics against the stock market. However, when the BIST100 index depreciates by 5%, Ethereum, silver and Government Bonds show strong safe-haven characteristics, US dollar and Euro show weak safe-haven characteristics. When the BIST100 index depreciates by 2.5%, Bitcoin, gold and DJIMTR show weak safe haven asset characteristics. If BIST100 depreciates by 1%, gold and Government Bonds show strong safe-haven characteristics, and Bitcoin, Ethereum, Silver, the US dollar and Euro show weak safe-haven characteristics.