Blockchain Papers

Kuo Zhou, Jing Huang, Honggui Han, Bei Gong · 7 authors

No abstract is available for this record.

Prabhat Kumar, Randhir Kumar, Moayad Aloqaily, A.K.M. Najmul Islam

The next-generation digital revolution is anticipated to be the convergence of Consumer Internet of Things (CIoT) platforms and Metaverse. The use of Metaverse in CIoT can offer a hyper-spatiotemporal, self-sustaining 3D virtual shared space for people to interact, work, and play. Despite the hype around CIoT-inspired Metaverse, security and privacy concerns are seen as the two biggest obstacles in the communication infrastructure and information gathering procedures. The eXplainable Artificial Intelligence (XAI) and blockchain have the potential to reshape and transform the CIoT-inspired Metaverse by bringing significant enhancements in terms of explainability, interpretability, transparency, traceability, and immutability regarding data and communications. In this paper, we first discuss about the security and privacy issues in CIoT-inspired Metaverse. Second, we discuss the importance and properties of XAI and blockchain with a use case to demonstrate the benefits of our proposed architecture to tackle the aforementioned obstacles. Finally, we highlight the future research directions in building futuristic CIoT-inspired Metaverse.

Zeqin Liao, Sicheng Hao, Yuhong Nan, Zibin Zheng

Smart contracts written in Solidity are widely used in different blockchain platforms such as Ethereum, TRON and BNB Chain. One of the unique designs in Solidity smart contracts is its state-reverting mechanism for error handling and access control. Unfortunately, a number of recent security incidents showed that adversaries also utilize this mechanism to manipulate critical states of smart contracts, and hence, bring security consequences such as illegal profit-gain and Deny-of-Service (DoS). In this paper, we call such vulnerabilities as the State-reverting Vulnerability (SRV). Automatically identifying SRVs poses unique challenges, as it requires an in-depth analysis and understanding of the state-dependency relations in smart contracts. This paper presents SmartState, a new framework for detecting state-reverting vulnerability in Solidity smart contracts via fine-grained state-dependency analysis. SmartState integrates a set of novel mechanisms to ensure its effectiveness. Particularly, Smart-State extracts state dependencies from both contract bytecode and historical transactions. Both of them are critical for inferring dependencies related to SRVs. Further, SmartState models the generic patterns of SRVs (i.e., profit-gain and DoS) as SRV indicators, and hence effectively identify SRVs based on the constructed state-dependency graph. To evaluate SmartState, we manually annotated a ground-truth dataset which contains 91 SRVs in the real world. Evaluation results showed that SmartState achieves a precision of 87.23% and a recall of 89.13%. In addition, SmartState successfully identifies 406 new SRVs from 47,351 real-world smart contracts. 11 of these SRVs are from popular smart contracts with high transaction amounts (i.e., top 2000). In total, our reported SRVs affect a total amount of digital assets worth 428,600 USD.

Nojan Sheybani, Zahra Ghodsi, Ritvik Kapila, Farinaz Koushanfar

Training contemporary AI models requires investment in procuring learning data and computing resources, making the models intellectual property of the owners. Popular model watermarking solutions rely on key input triggers for detection; the keys have to be kept private to prevent discovery, forging, and removal of the hidden signatures. We present ZKROWNN, the first automated end-to-end framework utilizing Zero-Knowledge Proofs (ZKP) that enable an entity to validate their ownership of a model, while preserving the privacy of the watermarks. ZKROWNN permits a third party client to verify model ownership in less than a second, requiring as little as a few KBs of communication.

Anzhelika Mezina, Aleksandr Ometov

The development of Distributed Ledger Technology (DLT) is pushing toward automating decentralized data exchange processes. One of the key components of this evolutionary step is facilitating smart contracts that, in turn, come with several additional vulnerabilities. Despite the existing tools for analyzing smart contracts, keeping these systems running and preserving performance while maintaining a decent level of security in a constantly increasing number of contracts becomes challenging. Machine Learning (ML) methods could be utilized for analyzing and detecting vulnerabilities in DLTs. This work proposes a new ML-based two-phase approach for the detection and classification of vulnerabilities in smart contracts. Firstly, the system’s operation is set up to filter the valid contracts. Secondly, it focuses on detecting a vulnerability type, if any. In contrast to existing approaches in this field of research, our algorithm is more focused on vulnerable contracts, which allows to save time and computing resources in the production environment. According to the results, it is possible to detect vulnerability types with an accuracy of 0.9921, F1 score of 0.9902, precision of 0.9883, and recall of 0.9921 within reasonable execution time, which could be suitable for integrating existing DLTs.

Weimin Chen, Zihan Sun, Haoyu Wang, Xiapu Luo · 6 authors

WebAssembly (Wasm) smart contracts have shown growing popularity across blockchains (e.g., EOSIO and NEAR) recently. Wasm smart contracts have been suffering from various attacks exploiting their vulnerabilities. Even worse, few developers released the source code of their Wasm smart contracts for security review, raising the bar for uncovering vulnerable contracts. Although a few approaches have been proposed to detect vulnerable Wasm smart contracts, they have several major limitations, e.g., low code coverage, low accuracy and lack of scalability, unable to produce exploit payloads, etc. To fill the gap, we design and implement WASAI,a new concolic fuzzer for uncovering vulnerabilities in Wasm smart contract. We conduct extensive experiments to evaluate WASAI,and the results show that it outperforms the state-of-the-art methods. WASAI achieves 2x code coverage than the baselines and surpasses them in detection accuracy, with an F1-measure of 99.2%. Applying WASAI to all deployed smart contracts in the wild, we find that over 707 smart contracts are vulnerable. One Fake EOS vulnerability reported to the EOSIO ecosystem was recently assigned a CVE identifier (CVE-2022-27134).

Xiguo Gu, Huiwen Yang, Shifan Liu, Zhanqi Cui

Smart contracts are programs running on the blockchain.In recent years, due to the continuous occurrence of smart contract security accidents, how to effectively detect vulnerabilities in smart contracts has received extensive attention.Machine learning-based vulnerability detection techniques have the advantage of not requiring expert rules.However, existing approaches have limitations in identifying vulnerabilities caused by version updates of smart contract compilers.In this paper, we propose OC-Detector, a smart contract vulnerabilities detection approach based on opcode instruction clustering.OC-Detector learns the characteristics of opcode instructions to cluster them and replaces opcode instructions belonging to the same cluster with the cluster number.After that, the similarity is calculated against the contract in the vulnerability database to identify vulnerabilities.Experimental results demonstrate that OC-Detector improves the F 1 value of detecting vulnerabilities from 0.04 to 0.40 compared to DC-Hunter, Securify, SmartCheck, and Osiris.Additionally, compared to DC-Hunter, F 1 value is improved by 0.27 when detecting vulnerabilities in smart contracts compiled by different version compilers.

Chaofan Shou, Shangyin Tan, Koushik Sen

Smart contracts are critical financial instruments, and their security is of utmost importance. However, smart contract programs are difficult to fuzz due to the persistent blockchain state behind all transactions. Mutating sequences of transactions are complex and often lead to a suboptimal exploration for both input and program spaces. In this paper, we introduce a novel snapshot-based fuzzer ItyFuzz for testing smart contracts. In ItyFuzz, instead of storing sequences of transactions and mutating from them, we snapshot states and singleton transactions. To explore interesting states, ItyFuzz introduces a dataflow waypoint mechanism to identify states with more potential momentum. ItyFuzz also incorporates comparison waypoints to prune the space of states. By maintaining snapshots of the states, ItyFuzz can synthesize concrete exploits like reentrancy attacks quickly. Because ItyFuzz has second-level response time to test a smart contract, it can be used for on-chain testing, which has many benefits compared to local development testing. Finally, we evaluate ItyFuzz on real-world smart contracts and some hacked on-chain DeFi projects. ItyFuzz outperforms existing fuzzers in terms of instructional coverage and can find and generate realistic exploits for on-chain projects quickly.

Junjie Hu, Zhe Jiang, Chunxiang Xu

Bitcoin-NG is an extensible blockchain protocol based on the same trust model as Bitcoin. It divides each epoch into one Key-Block and multiple Micro-Blocks, effectively improving transaction processing capacity. Bitcoin-NG adopts a special incentive mechanism (i.e., the transaction fees in each epoch are split to the current and next leader) to maintain its security. However, there are some limitations to the existing incentive analysis of Bitcoin-NG in recent works. First, the incentive division method of Bitcoin-NG only includes some specific mining attack strategies of adversary, while ignoring more stubborn attack strategies. Second, once adversaries find a whale transaction, they will deviate from honest mining strategy to obtain extra reward. In this paper, we are committed to solving these two limitations. First, we propose a novel mining strategy named Greedy-Mine attack. Then, we formulate a Markov Decision Process (MDP) model to analyze the competition of honest miners and adversaries. Furthermore, we analysis the extra reward of adversaries and summarize the mining power proportion range required for malicious adversaries to launch Greedy-Mine to obtain extra returns. Finally, we make a backward-compatibility progressive modification to Bitcoin-NG protocol that would raise the threshold of propagation factor from 0 to 1. Meanwhile, we get the winning condition of adversaries when adopting Greedy-Mine, compared with honest mining. Simulation and experimental results indicate that Bitcoin-NG is not incentive compatible, which is vulnerable to Greedy-Mine attack.

Deepali Awasthi, Shagun Chauhan, Swati Lohiya, Mahima Kaushik

The increased demand for data availability in every industry is driving individuals to exchange and store data on centralized platforms such as clouds so that the intended audience may access it. To facilitate data exchange and storage in the medical industry, organizations and patients are building cloud platforms. However, the most pressing issue that everyone faces is data protection and security. Here, we describe many techniques that are available to protect the system and meet the requirement for data privacy preservation in the medical industry. Some algorithms are Zero-Knowledge Proof, Principal Component Analysis and Random Projection, Generative Adversarial Networks, blockchain and cloud computing, Quasi-Identifier Recognition, Q-learning Neural Network, digital signature, and others.

Zhujun Wang, Lejun Zhang, Ran Guo, Guopeng Wang · 9 authors

No abstract is available for this record.

Emanuele Antonio Napoli, Valentina Gatteschi

The growing number of exploits and hacks on the Ethereum blockchain has led to the development of powerful smart contract vulnerability detection tools and the frequent patching of the smart contract’s programming languages (such as Solidity). At the same time, an ever-increasing number of people are interested in blockchain and smart contract-related topics and willing to build and deploy their own Decentralized Applications (dApp). However, learning a new programming language and its best practices as long as how to actually deploy a smart contract on the blockchain is a difficult task even for experienced developers. Recently, ChatGPT, a new user-friendly deep learning tool, has been released to improve the ability of non-skilled users to write high-quality code and in general, to boost the performances of developers in key tasks related to code writing (i.e., writing functions, explaining runtime errors, fixing bugs, etc.). This paper aims to measure the capabilities of ChatGPT in fixing vulnerable smart contracts and to assess the effectiveness of this tool, determining whether it can be a valuable aid for those who want to correct their own smart contract or want to reuse existing ones by first checking their status and eventually fix their vulnerability. In particular, we asked ChatGPT to fix 143 smart contracts with well-known labeled vulnerabilities. We considered a vulnerability as "fixed" if the code corrected by ChatGPT no longer contained the vulnerability (for this purpose, we exploited Slither, one of the state-of-the-art tools for smart contracts vulnerability detection to check the status of the original and the corrected smart contracts). As a result we obtained that ChatGPT was able to fix bugs and vulnerable smart contracts on average the 57.1% of the time with an increase of +1.4% when a description of the bug was provided in addition to the smart contract’s source code.

Georgios Palaiokrassas, Sandro Scherrers, Iason Ofeidis, Leandros Tassiulas

Since the inception of permissionless blockchains with Bitcoin in 2008, it became apparent that their most well-suited use case is related to making the financial system and its advantages available to everyone seamlessly without depending on any trusted intermediaries. Smart contracts across chains provide an ecosystem of decentralized finance (DeFi), where users can interact with lending pools, Automated Market Maker (AMM) exchanges, stablecoins, derivatives, etc. with a cumulative locked value which had exceeded 160B USD. While DeFi comes with high rewards, it also carries plenty of risks. Many financial crimes have occurred over the years making the early detection of malicious activity an issue of high priority. The proposed framework introduces an effective method for extracting a set of features from different chains, including the largest one, Ethereum and it is evaluated over an extensive dataset we gathered with the transactions of the most widely used DeFi protocols (23 in total, including Aave, Compound, Curve, Lido, and Yearn) based on a novel dataset in collaboration with Covalent. Different Machine Learning methods were employed, such as XGBoost and a Neural Network for identifying fraud accounts detection interacting with DeFi and we demonstrate that the introduction of novel DeFi-related features, significantly improves the evaluation results, where Accuracy, Precision, Recall, F1-score and F2-score where utilized.

Liehuang Zhu, Qi Liu, Zhuo Chen, Can Zhang · 6 authors

Covert channels serve the construction of cyberspace security. By realizing the secure transmission of data, it is widely used in political and financial fields. Blockchain covert channels have higher reliability and concealment compared to traditional network-based covert channels. However, existing blockchain covert storage channels need to create a large number of transactions to transmit covert information. Creating transactions requires a transation fee, which means that the implementation of blockchain covert storage channels requires a high cost. Besides, created transactions remain on-chain permanently, leading to the threat of covert information being detected. To overcome these limitations, we propose a blockchain covert timing channel framework. Specifically, we utilize inv and getdata messages in the Bitcoin transaction broadcast as carriers and propose three modulation modes to achieve covert channels without cost and leaving no trace. We evaluate the concealment of our modes by K-S, KLD tests, and machine learning approaches. Experimental results show the indistinguishability between traffic carrying covert information and normal traffic. Our channels promise a capacity of 2.4 bit/s.

Prashant Agrawal, Abhinav Nakarmi, Mahavir Prasad Jhawar, Subodh Sharma · 5 authors

We introduce the notion of \emph{traceable mixnets}. In a traditional mixnet, multiple mix-servers jointly permute and decrypt a list of ciphertexts to produce a list of plaintexts, along with a proof of correctness, such that the association between individual ciphertexts and plaintexts remains completely hidden. However, in many applications, the privacy-utility tradeoff requires answering some specific queries about this association, without revealing any information beyond the query result. We consider queries of the following types: a) given a ciphertext in the mixnet input list, whether it encrypts one of a given subset of plaintexts in the output list, and b) given a plaintext in the mixnet output list, whether it is a decryption of one of a given subset of ciphertexts in the input list. Traceable mixnets allow the mix-servers to jointly prove answers to the above queries to a querier such that neither the querier nor a threshold number of mix-servers learn any information beyond the query result. Further, if the querier is not corrupted, the corrupted mix-servers do not even learn the query result. We first comprehensively formalise these security properties of traceable mixnets and then propose a construction of traceable mixnets using novel distributed zero-knowledge proofs (ZKPs) of set membership and of a statement we call reverse set membership. Although set membership has been studied in the single-prover setting, the main challenge in our distributed setting lies in making sure that none of the mix-servers learn the association between ciphertexts and plaintexts during the proof. We implement our distributed ZKPs and show that they are faster than state-of-the-art by at least one order of magnitude.

Wenyuan Yang, Yuguo Yin, Gongxi Zhu, Hanlin Gu · 7 authors

Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other. The need of protecting such federated models from being plagiarized or misused, therefore, motivates us to propose a provable secure model ownership verification scheme using zero-knowledge proof, named FedZKP. It is shown that the FedZKP scheme without disclosing credentials is guaranteed to defeat a variety of existing and potential attacks. Both theoretical analysis and empirical studies demonstrate the security of FedZKP in the sense that the probability for attackers to breach the proposed FedZKP is negligible. Moreover, extensive experimental results confirm the fidelity and robustness of our scheme.

Mohammad Khodadadi, Jafar Tahmoresnezhad

With blockchain technology rapidly progress, the smart contracts have become a common tool in a number of industries including finance, healthcare, insurance and gaming. The number of smart contracts has multiplied, and at the same time, the security of smart contracts has drawn considerable attention due to the monetary losses brought on by smart contract vulnerabilities. Existing analysis techniques are capable of identifying a large number of smart contract security flaws, but they rely too much on rigid criteria established by specialists, where the detection process takes much longer as the complexity of the smart contract rises. In this paper, we propose HyMo as a multi-modal hybrid deep learning model, which intelligently considers various input representations to consider multimodality and FastText word embedding technique, which represents each word as an n-gram of characters with BiGRU deep learning technique, as a sequence processing model that consists of two GRUs to achieve higher accuracy in smart contract vulnerability detection. The model gathers features using various deep learning models to identify the smart contract vulnerabilities. Through a series of studies on the currently publicly accessible dataset such as ScrawlD, we show that our hybrid HyMo model has excellent smart contract vulnerability detection performance. Therefore, HyMo performs better detection of smart contract vulnerabilities against other approaches.

Monika di Angelo, Gernot Salzer

Smart contracts are small programs on the blockchain that often handle valuable assets. Vulnerabilities in smart contracts can be costly, as time has shown over and over again. Countermeasures are high in demand and include best practice recommendations as well as tools supporting development, program verification, and post-deployment analysis. Many tools focus on detecting the absence or presence of a subset of the known vulnerabilities, delivering results of varying quality. Most comparative tool evaluations resort to selecting a handful of tools and testing them against each other. In the best case, the evaluation is based on a smallish ground truth. For Ethereum, there are commendable efforts by several author groups to manually classify contracts. However, a comprehensive ground truth is still lacking. In this work, we construct a ground truth based on publicly available benchmark sets for Ethereum smart contracts with manually checked ground truth data. We develop a method to unify these sets. Additionally, we devise strategies for matching entries that pertain to the same contract, such that we can determine overlaps and disagreements between the sets and consolidate the disagreements. Finally, we assess the quality of the included ground truth sets. Our work reduces inconsistencies, redundancies, and incompleteness while increasing the number of data points and heterogeneity.

Tamer Abdelaziz, Aquinas Hobor

We introduce SCooLS, our Smart Contract Learning (Semi-supervised) engine. SCooLS uses neural networks to analyze Ethereum contract bytecode and identifies specific vulnerable functions. SCooLS incorporates two key elements: semi-supervised learning and graph neural networks (GNNs). Semi-supervised learning produces more accurate models than unsupervised learning, while not requiring the large oracle-labeled training set that supervised learning requires. GNNs enable direct analysis of smart contract bytecode without any manual feature engineering, predefined patterns, or expert rules. SCooLS is the first application of semi-supervised learning to smart contract vulnerability analysis, as well as the first deep learning-based vulnerability analyzer to identify specific vulnera-ble functions. SCooLS's performance is better than existing tools, with an accuracy level of 98.4%, an F1 score of 90.5%, and an exceptionally low false positive rate of only 0.8%. Furthermore, SCooLS is fast, analyzing a typical function in 0.05 seconds. We leverage SCooLS's ability to identify specific vulnerable functions to build an exploit generator, which was successful in stealing Ether from 76.9% of the true positives.

Wang Nai, Junjun Chen, Di Wu, Wencheng Yang · 6 authors

No abstract is available for this record.

Michael Rodler, David Paaßen, Wenting Li, Lukas Bernhard · 7 authors

Smart contracts are increasingly being used to manage large numbers of high-value cryptocurrency accounts. There is a strong demand for automated, efficient, and comprehensive methods to detect security vulnerabilities in a given contract. While the literature features a plethora of analysis methods for smart contracts, the existing proposals do not address the increasing complexity of contracts. Existing analysis tools suffer from false alarms and missed bugs in today's smart contracts that are increasingly defined by complexity and interdependencies. To scale accurate analysis to modern smart contracts, we introduce EF/CF, a high-performance fuzzer for Ethereum smart contracts. In contrast to previous work, EF/CF efficiently and accurately models complex smart contract interactions, such as reentrancy and cross-contract interactions, at a very high fuzzing throughput rate. To achieve this, EF/CF transpiles smart contract bytecode into native C++ code, thereby enabling the reuse of existing, optimized fuzzing toolchains. Furthermore, EF/CF increases fuzzing efficiency by employing a structure-aware mutation engine for smart contract transaction sequences and using a contract's ABI to generate valid transaction inputs. In a comprehensive evaluation, we show that EF/CF scales better -- without compromising accuracy -- to complex contracts compared to state-of-the-art approaches, including other fuzzers, symbolic/concolic execution, and hybrid approaches. Moreover, we show that EF/CF can automatically generate transaction sequences that exploit reentrancy bugs to steal Ether.

Tao Zhang, Bingyu Li, Yan Zhu, Tianxu Han · 5 authors

No abstract is available for this record.

Sai Sirisha Maddula

Ethereum intelligent contract security must be guaranteed since these decentralized apps oversee large-scale financial transactions independently. To strengthen the dependability and credibility of Ethereum smart contracts, this paper assesses existing methods for finding weaknesses in them. The primary goals are to evaluate how well hybrid approaches, formal verification, dynamic analysis, and static analysis find vulnerabilities. Methodologically, a thorough assessment of available resources and instruments was carried out to evaluate the advantages and disadvantages of each approach. Important discoveries show that although static analysis covers a large area, it ignores runtime-specific problems and produces false positives. While highly effective in finding runtime vulnerabilities, dynamic analysis is resource-intensive. High assurance is provided by formal verification, although it is complex and resource-intensive. Hybrid approaches combine several approaches to provide a well-rounded strategy but must be used carefully. The policy implications emphasize that to limit risks effectively, it is crucial to embrace multifaceted security techniques, set explicit norms, and promote easily accessible verification tools. This research advances our knowledge of smart contract security and guides policymakers and developers on securing blockchain applications.

Huadong Feng, Xiaolei Ren, Qi-Ping Wei, Yu Lei · 7 authors

A smart contract is often used to handle financial transactions. Unlike traditional programs, contract codes cannot be changed after deployment. It is crucial to test smart contracts thoroughly before deployment. In this paper, we present a fuzzing approach to testing smart contracts. Our fuzzing approach utilizes constraint solving, selective state exploration, and combinatorial testing to improve code coverage. Constraint solving generates test inputs that meet preconditions in a smart contract. Selective state exploration allows different state-dependent behaviors to be exercised while alleviating the state explosion problem. Combinatorial testing is used to exercise parameter interactions in a systematic manner. We implemented our approach in a tool called MagicMirror and evaluated our approach using more than 2,000 contracts. The experimental results show that MagicMirror effectively achieves high code coverage and detects vulnerabilities.