Smart contracts are pieces of code that reside inside the blockchains and can be triggered to execute any transaction when specifically predefined conditions are satisfied. Being commonly used for commercial transactions in blockchain makes the security of smart contracts particularly important. Over the last few years, we have seen a great deal of academic and practical interest in detecting and fixing the bugs in smart contracts written by Solidity. But little is known about the real bug fixes in Solidity smart contract projects. To understand the bug fixes and enrich the knowledge of bug fixes in real-world projects, we conduct an empirical study on historical bug fixes from 46 real-world Solidity smart contract projects in this paper. We provide a multi-faceted discussion and mainly explore the following four questions: File Type and Amount, Fix Complexity, Bug distribution, and Fix Patches. We distill four findings during the process to explore these four questions. Finally, based on these findings, we provide actionable implications to improve the current approaches to fixing bugs in Solidity smart contracts from three aspects: Automatic repair techniques, Analysis tools, and Solidity developers.
Tommaso Frassetto, Patrick Jauernig, David Koisser, David Kretzler · 7 authors
Smart contracts enable users to execute payments depending on complex program logic. Ethereum is the most notable example of a blockchain that supports smart contracts leveraged for countless applications including games, auctions and financial products. Unfortunately, the traditional method of running contract code on-chain is very expensive, for instance, on the Ethereum platform, fees have dramatically increased, rendering the system unsuitable for complex applications. A prominent solution to address this problem is to execute code off-chain and only use the blockchain as a trust anchor. While there has been significant progress in developing off-chain systems over the last years, current off-chain solutions suffer from various drawbacks including costly blockchain interactions, lack of data privacy, huge capital costs from locked collateral, or supporting only a restricted set of applications. \n \nIn this paper, we present POSE—a practical off-chain protocol for smart contracts that addresses the aforementioned shortcomings of existing solutions. POSE leverages a pool of Trusted Execution Environments (TEEs) to execute the computation efficiently and to swiftly recover from accidental or malicious failures. We show that POSE provides strong security guarantees even if a large subset of parties is corrupted. We evaluate our proof-of-concept implementation with respect to its efficiency and effectiveness.
Smart contracts have been widely and rapidly used to automate financial and business transactions together with blockchains, helping people make agreements while minimizing trusts. With millions of smart contracts deployed on blockchain, various bugs and vulnerabilities in smart contracts have emerged. Following the rapid development of deep learning, many recent studies have used deep learning for vulnerability detection to conduct security checks before deploying smart contracts. These approaches show effective results on detecting whether a smart contract is vulnerable or not whereas their results on locating suspicious statements responsible for the detected vulnerability are still unsatisfactory.
The growth in the popularity of smart contracts has been accompanied by a rise in security attacks targeting smart contracts, which have led to financial losses of millions of dollars and erosion of trust. To enable developers discover vulnerabilities in smart contracts, several static analysis tools have been proposed. However, despite the numerous bug-finding tools, security vulnerabilities abound in smart contracts, and developers rely on finding vulnerabilities manually. Our goal in this dissertation study is to expand the space of security vulnerabilities detection by proposing effective static analysis approaches for smart contracts. We study the effectiveness of the existing static analysis tools and propose solutions for security vulnerabilities detection relying on analyzing the dependency of the contract code on user inputs that lead to security vulnerabilities. Our results of evaluating static analysis tools show that existing static tools for smart contracts have significant false-negatives and false-positives. Further, the results show that our first vulnerability detection approach achieves a significant improvement in the effectiveness of detecting vulnerabilities compared to the prior work.
Smart contracts are gaining popularity as a means to support transparent, traceable, and self-executing decentralized applications, which enable the exchange of value in a trustless environment. Developers of smart contracts rely on various libraries, such as OpenZeppelin for Solidity contracts, to improve application quality and reduce development costs. The API documentations of these libraries are important sources of information for developers who are unfamiliar with the APIs. Yet, maintaining high-quality documentations is non-trivial, and errors in documentations may place barriers for developers to learn the correct usages of APIs. In this paper, we propose a technique, DocCon, to detect inconsistencies between documentations and the corresponding code for Solidity smart contract libraries. Our fact-based approach allows inconsistencies of different severity levels to be queried, from a database containing precomputed facts about the API code and documentations. DocCon successfully detected high-priority API documentation errors in popular smart contract libraries, including mismatching parameters, missing requirements, outdated descriptions, etc. Our experiment result shows that DocCon achieves good precision and is applicable to different libraries: 29 and 22 out of our reported 40 errors have been confirmed and fixed by library developers so far.
As computer programs run on top of blockchain, smart contracts have proliferated a myriad of decentralized applications while bringing security vulnerabilities, which may cause huge financial losses. Thus, it is crucial and urgent to detect the vulnerabilities of smart contracts. However, existing fuzzers for smart contracts are still inefficient to detect sophisticated vulnerabilities that require specific vulnerable transaction sequences to trigger. To address this challenge, we propose a novel vulnerability-guided fuzzer based on reinforcement learning, namely RLF, for generating vulnerable transaction sequences to detect such sophisticated vulnerabilities in smart contracts. In particular, we firstly model the process of fuzzing smart contracts as a Markov decision process to construct our reinforcement learning framework. We then creatively design an appropriate reward with consideration of both vulnerability and code coverage so that it can effectively guide our fuzzer to generate specific transaction sequences to reveal vulnerabilities, especially for the vulnerabilities related to multiple functions. We conduct extensive experiments to evaluate RLF’s performance. The experimental results demonstrate that our RLF outperforms state-of-the-art vulnerability-detection tools (e.g., detecting 8%-69% more vulnerabilities within 30 minutes).
These days, businesses, in both online and offline, have started accepting cryptocurrencies as payment methods. Even in countries like <italic>El Salvador</italic>, cryptocurrencies are recognized as fiat currencies. Meanwhile, publicly known, but not patched software vulnerabilities are security threats to not only software users but also to our society in general. As the status of cryptocurrencies has gradually increased, the impact of security vulnerabilities related to cryptocurrencies on our society has increased as well. In this paper, we first analyze vulnerabilities from the two major cryptocurrency vendors of Bitcoin and Ethereum in a quantitative manner with the respect to the CVSS, to see how the vulnerabilities are roughly structured in those systems. Then we introduce a modified AML vulnerability discovery model for the vulnerability datasets from the two vendors, after showing the original AML dose not accurately represent the vulnerability discovery trends on the datasets. The analysis shows that the modified model performs better than the original AML model for the vulnerability datasets from the major cryptocurrencies.
Coinhive released its browser-based cryptocurrency mining code in September 2017, and vicious web page writers, called vicious miners hereafter, began to embed mining JavaScript code into their web pages, called mining pages hereafter. As a result, browser users surfing these web pages will benefit mine cryptocurrencies unwittingly for the vicious miners using the CPU resources of their devices. The above activity, called Cryptojacking, has become one of the most common threats to web browser users. As mining pages influence the execution efficiency of regular programs and increase the electricity bills of victims, security specialists start to provide methods to block mining pages. Nowadays, using a blocklist to filter out mining scripts is the most common solution to this problem. However, when the number of new mining pages increases quickly, and vicious miners apply obfuscation and encryption to bypass detection, the detection accuracy of blacklist-based or feature-based solutions decreases significantly. This paper proposes a solution, called MinerGuard, to detect mining pages. MinerGuard was designed based on the observation that mining JavaScript code consumes a lot of CPU resources because it needs to execute plenty of computation. MinerGuard does not need to update data used for detection frequently. On the contrary, blacklist-based or feature-based solutions must update their blocklists frequently. Experimental results show that MinerGuard is more accurate than blacklist-based or feature-based solutions in mining page detection. MinerGuard’s detection rate for mining pages is 96%, but MinerBlock, a blacklist-based solution, is 42.85%. Moreover, MinerGuard can detect 0-day mining pages and scripts, but the blacklist-based and feature-based solutions cannot.
In recent years, sharing threat information has been one of the most suggested solutions for combating the ever-increasing number of cyberattacks, which stem from the system-wide adoption of Information and Communication Technology (ICT) and consequently endangers the digital and physical assets of organizations. Several solutions, however, were proposed to facilitate data exchange between different systems, but none were able to address the main challenges of threat sharing such as trust, privacy, interoperability, and automation in a single solution. To address these issues, this paper presents a secure and efficient threat information sharing system that leverages Trusted Automated Exchange of Intelligence Information (TAXIITM) standard and private blockchain technology to automate the threat sharing procedure while offering privacy, data integrity, and interoperability. The extensive evaluation of the solution implementation indicates its capability to offer secure communication between participants without sacrificing data privacy and overall performance as opposed to existing solutions.
Bitcoin has been subject to illicit activities more often than probably any other financial assets, due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all the three properties of (I) early detection, (II) good interpretability, and (III) versatility for various illicit activities. However, existing solutions cannot meet all these requirements, as most of them heavily rely on deep learning without satisfying interpretability and are only available for retrospective analysis of a specific illicit type. First, we present asset transfer paths, which aim to describe addresses' early characteristics. Next, with a decision tree based strategy for feature selection and segmentation, we split the entire observation period into different segments and encode each as a segment vector. After clustering all these segment vectors, we get the global status vectors, essentially the basic unit to describe the whole intention. Finally, a hierarchical self-attention predictor predicts the label for the given address in real time. A survival module tells the predictor when to stop and proposes the status sequence, namely intention. % With the type-dependent selection strategy and global status vectors, our model can be applied to detect various illicit activities with strong interpretability. The well-designed predictor and particular loss functions strengthen the model's prediction speed and interpretability one step further. Extensive experiments on three real-world datasets show that our proposed algorithm outperforms state-of-the-art methods. Besides, additional case studies justify our model can not only explain existing illicit patterns but can also find new suspicious characters.
Recently, security issues of smart contracts are arising great attention due to the enormous financial loss caused by vulnerability attacks. There is an increasing need to detect similar codes for hunting vulnerability with the increase of critical security issues in smart contracts. Binary similarity detection that quantitatively measures the given code diffing has been widely adopted to facilitate critical security analysis. However, due to the difference between common programs and smart contract, such as diversity of bytecode generation and highly code homogeneity, directly adopting existing graph matching and machine learning based techniques to smart contracts suffers from low accuracy, poor scalability and the limitation of binary similarity on function level. Therefore, this paper investigates graph neural network to detect smart contract binary code similarity at the program level, where we conduct instruction-level normalization to reduce the noise code for smart contract pre-processing and construct contract control flow graphs to represent smart contracts. In particular, two improved Graph Convolutional Network (GCN) and Message Passing Neural Network (MPNN) models are explored to encode the contract graphs into quantitatively vectors, which can capture the semantic information and the program-wide control flow information with temporal orders. Then we can efficiently accomplish the similarity detection by measuring the distance between two targeted contract embeddings. To evaluate the effectiveness and efficient of our proposed method, extensive experiments are performed on two real-world datasets, i.e., smart contracts from Ethereum and Enterprise Operation System (EOS) blockchain-based platforms. The results show that our proposed approach outperforms three state-of-the-art methods by a large margin, achieving a great improvement up to 6.1% and 17.06% in accuracy.
Fahed Alkhabbas, Mohammed Alsadi, Sadi Alawadi, Feras M. Awaysheh · 6 authors
Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems' environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems' security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach's feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems' constituents to learn about security threats in their environments collaboratively.
The Internet of Things (IoT) has widely expanded due to its advantages in enhancing the business, industrial, and social ecosystems. Nevertheless, IoT infrastructure is susceptible to several cyber-attacks due to the endpoint devices' restrictions in computation, storage, and communication capacity. As such, distributed denial-of-service (DDoS) attacks pose a serious threat to the security of the IoT. Attackers can easily utilize IoT devices as part of botnets to launch DDoS attacks by taking advantage of their flaws. This paper proposes an Ethereum blockchain model to detect and prevent DDoS attacks against IoT systems. Additionally, the proposed system can be used to resolve the single points of failure (dependencies on third parties) and privacy and security in IoT systems. First, we propose implementing a decentralized platform in place of current centralized system solutions to prevent DDoS attacks on IoT devices at the application layer by authenticating and verifying these devices. Second, we suggest tracing and recording the IP address of malicious devices inside the blockchain to prevent them from connecting and communicating with the IoT networks. The system performance has been evaluated by performing 100 experiments to evaluate the time taken by the authentication process. The proposed system highlights two messages with a time of 0.012 ms: the first is the request transmitted from the IoT follower device to join the blockchain, and the second is the blockchain response. The experimental evaluation demonstrated the superiority of our system because there are fewer I/O operations in the proposed system than in other related works, and thus it runs substantially faster.
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Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Learning heterogeneous graphs consisting of different types of nodes and edges enhances the results of homogeneous graph techniques. An interesting example of such graphs is control-flow graphs representing possible software code execution flows. As such graphs represent more semantic information of code, developing techniques and tools for such graphs can be highly beneficial for detecting vulnerabilities in software for its reliability. However, existing heterogeneous graph techniques are still insufficient in handling complex graphs where the number of different types of nodes and edges is large and variable. This paper concentrates on the Ethereum smart contracts as a sample of software codes represented by heterogeneous contract graphs built upon both control-flow graphs and call graphs containing different types of nodes and links. We propose MANDO, a new heterogeneous graph representation to learn such heterogeneous contract graphs’ structures. MANDO extracts customized meta-paths, which compose relational connections between different types of nodes and their neighbors. Moreover, it develops a multi-metapath heterogeneous graph attention network to learn multi-level embeddings of different types of nodes and their metapaths in the heterogeneous contract graphs, which can capture the code semantics of smart contracts more accurately and facilitate both fine-grained line-level and coarse-grained contract-level vulnerability detection. Our extensive evaluation of large smart contract datasets shows that MANDO improves the vulnerability detection results of other techniques at the coarse-grained contract level. More importantly, it is the first learning-based approach capable of identifying vulnerabilities at the fine-grained line-level, and significantly improves the traditional code analysis-based vulnerability detection approaches by 11.35% to 70.81% in terms of F1-score.
Cyber Threat Intelligence (CTI) is the knowledge of cyber and physical threats that help mitigate potential cyber attacks. The rapid evolution of the current threat landscape has seen many organisations share CTI to strengthen their security posture for mutual benefit. However, in many cases, CTI data contains attributes (e.g., software versions) that have the potential to leak sensitive information or cause reputational damage to the sharing organisation. While current approaches allow restricting CTI sharing to trusted organisations, they lack solutions where the shared data can be verified and disseminated `differentially' (i.e., selective information sharing) with policies and metrics flexibly defined by an organisation. In this paper, we propose a blockchain-based CTI sharing framework that allows organisations to share sensitive CTI data in a trusted, verifiable and differential manner. We discuss the limitations associated with existing approaches and highlight the advantages of the proposed CTI sharing framework. We further present a detailed proof of concept using the Ethereum blockchain network. Our experimental results show that the proposed framework can facilitate the exchange of CTI without creating significant additional overheads.
A smart contract is a pivotal notion in blockchain technology. Distributed applications contain smart contracts verifying the fulfillment of the conditions, which determine the execution of transactions between the blockchain network nodes. Those software-controlled logical conditions are called verification rules. As the number of conditions increases, the complexity of smart contract testing rapidly grows. This paper aims to propose a smart contract testing pattern that significantly limits the needed number of test cases. For evaluation expression with four verification rules, the pattern usage reduces the number of test cases by 68.75% in relation to the full coverage of logical value combinations. With the increase in the number of logical conditions, not only the number of test cases but also their percentage decreases. Starting from seven verification rules in the evaluation expression, the percentage reduction of test cases exceeds 90%. As a result, the cost of preparing and maintaining test case suites may be substantially cut. It should be emphasized that test execution time can be reduced even by 3 orders of magnitude (from seconds to milliseconds). Such an approach is highly important for regression testing, especially when used in continuous software integration, delivery, and deployment approaches.
Fausto Spoto, Sara Migliorini, Mauro Gambini, Andrea Benini
Abstract This paper shows that generic types ( generics ) are useful for writing more abstract and more general smart contracts, but this comes with some security risks, reporting a concrete security issue found while using generics for writing smart contracts that implement shared entities for the Hotmoka blockchain. That issue can be used to steal the remuneration of validator nodes. This paper proposes a patch based on appropriate code rewriting. Namely, smart contracts are pieces of code that are deployed and executed in the context of a blockchain infrastructure in order to automatically enforce some effects when particular events occur. The writing of smart contracts is a complex and critical activity that can benefit from the use of high-level features of programming languages, and generics is one of them. In many programming languages, such as Java, generics are implemented by erasure , i.e. replaced by their upper bound type during compilation into bytecode. This is safe at source level, since the compiler takes care of checking that types are correct, before erasure. However, the erased types of the generated bytecode are consequently weaker. In a permissionless blockchain, where every user can call the bytecode of smart contracts installed by other users, these weaker types pose a risk of attack.
Yang Yang, Shangbin Han, Ping Xie, Yan Zhu · 8 authors
With the increasing demand for privacy protection in the blockchain, the universal zero-knowledge proof protocol has been developed and widely used. Because hash function is an important cryptographic primitive in a blockchain, the zero-knowledge proof of hash preimage has a wide range of application scenarios. However, it is hard to implement it due to the transformation of efficiency and execution complexity. Currently, there are only zero-knowledge proof circuits of some widely used hash functions that have been implemented, such as SHA256. SM3 is a Chinese hash function standard published by the Chinese Commercial Cryptography Administration Office for the use of electronic authentication service systems, and hence might be used in several cryptographic applications in China. As the national cryptographic hash function standard, the zero-knowledge proof circuit of SM3 (Chinese Commercial Cryptography) has not been implemented. Therefore, this paper analyzed the SM3 algorithm process, designed a new layered circuit structure, and implemented the SM3 hash preimage zero-knowledge proof circuit with a circuit size reduced by half compared to the automatic generator. Moreover, we proposed several extended practical protocols based on the SM3 zero-knowledge proof circuit, which is widely used in blockchain.
Along with the advancements in blockchain technology, many blockchain-based successful projects have been done mainly on the ethereum platform, most of which deal with transactions.Still, it also carries various risks when it comes to security, as evident from past attacks.Most big projects like uniswap, decentraland, and others use smart contracts, deployed on the ethereum platform, leading to similar projects via code reuse.Code reuse practice is quite frequent as a survey suggests 26% of contract code deployed is via code reuse.Smart contract code obfuscation techniques can be used on solidity code that is publicly verified, published (in the case of Ethereum), and on the deployment address.All the above techniques work by replacing characters with their random counterpart, known as statistical substitution.A statistical substitution is a process of transforming an input string into a new string where each character has been replaced by a random character drawn from a stock of all possible 'random' characters.Therefore, we proposed numerous methods in this paper to solve the above problems using various smart contract code obfuscation techniques.These techniques can be really useful in blockchain projects and can save millions of dollars to investors & companies by enhancing code security and preventing code reusability.Techniques mentioned in this paper when compared with other techniques.Our methods are not expensive to implement, very easy to use, and provide a developer-friendly selective increment in code complexity.
Cristina Regueiro, Ivan Gutierrez-Ag ́ Ag ́uero, Sergio Sergio, Santiago de Diego · 5 authors
This paper presents a protocol for Identity Management in Industrial IoT enabled devices, that is based on the principles of Self-Sovereign Identity. The Industry 4.0 transformation has led to the Industry sector digitalization and one of its major challenges is to uniquely identify the Industrial Internet of Things unattended devices. The digital identity management must allow increasing the security and control, and it has been evolving towards a model where the device acquires the responsibility for managing its own data through Self-Sovereign Identity. This paper studies why the Self-Sovereign Identity approach is suitable for the industrial IoT particularities, properly justifying its use. Furthermore, it analyzes the actors and roles involved in an industrial identity environment, and it addresses a protocol that defines how data should be exchanged over an Hyperledger Indy public permissioned Distributed Ledger Technology network as Sovrin. The paper applies the proposal to a reference use case, filling the gaps that are not currently specified in the literature for a successful Industrial Internet of Things identity management operation.
Jesús Rosa-Bilbao, Juan Boubeta-Puig, Adrian Rutle
Blockchain is a cutting-edge technology based on a distributed, secure and immutable ledger that facilitates the registration of transactions and the traceability of tangible and intangible assets without requiring central governance. The agreements between the nodes participating in a blockchain network are defined through smart contracts. However, the compilation, deployment, interaction and monitoring of these smart contracts is a barrier compromising the accessibility of blockchains by non-expert developers. To address this challenge, in this paper, we propose a low-code approach, called EDALoCo, that facilitates the development of event-driven applications for smart contract management. These applications make blockchain more accessible for software developers who are non-experts in this technology as these can be modeled through graphical flows, which specify the communications between data producers, data processors and data consumers. Specifically, we have enhanced the open-source Node-RED low-code platform with blockchain technology, giving support for the creation of user-friendly and lightweight event-driven applications that can compile and deploy smart contracts in a particular blockchain. Additionally, this platform extension allows users to interact with and monitor the smart contracts already deployed in a blockchain network, hiding the implementation details from non-experts in blockchain. This approach was successfully applied to a case study of COVID-19 vaccines to monitor and obtain the temperatures to which these vaccines are continuously exposed, to process them and then to store them in a blockchain network with the aim of making them immutable and traceable to any user. As a conclusion, our approach enables the integration of blockchain with the low-code paradigm, simplifying the development of lightweight event-driven applications for smart contract management. The approach comprises a novel open-source solution that makes data security, immutability and traceability more accessible to software developers who are non-blockchain experts.
Abstract The realization of fully autonomous vehicles in great scale is already in sight. However, to be safe and reliable, autonomous driving requires a significant amount of communication with cloud services, which are prone to many kinds of failures and malicious attacks. This paper introduces the use of AIngle, a novel distributed-ledger technology developed to guarantee real-time encrypted communication among IoT devices. Not only AIngle tackles various kinds of malicious attacks by design, such as Sybil attacks, but it also enables semantic distributed applications (Sem D Apps). Sem D Apps decentralize supporting driving services, such as traffic and route mapping, which are typically centralized in cloud services. AIngle provides a new paradigm for Sem D Apps, that will support future-proofed, fast, reliable, intelligent distributed applications for the autonomous vehicle industry.
This paper presents the design, implementation, and verification of a blockchain-based online electronic voting system that ensures accuracy and reliability in electronic voting and its application to various types of voting using blockchain technologies, such as distributed ledgers and smart contracts. Specifically, in this study, the connection between the electronic voting system and blockchain nodes is simplified using the REST API design, and the voting opening and counting information is designed to store the latest values in the distributed ledger in JSON format, using a smart contract that cannot be falsified. The developed electronic voting system can provide blockchain authentication, secret voting, forgery prevention, ballot verification, and push notification functions, all of which are currently not supported in existing services. Furthermore, the developed system demonstrates excellence on all evaluation items, including 101 transactions per second (TPS) of blockchain online authentication, 57.6 TPS of secret voting services, 250 TPS of forgery prevention cases, 547 TPS of read transaction processing, and 149 TPS of write transaction processing, along with 100% ballot verification service, secret ballot authentication, and encryption accuracy. Functional and performance verifications were obtained through an external test certification agency in South Korea. Our design allows for blockchain authentication, non-forgery of ballot counting data, and secret voting through blockchain-based distributed ledger technology. In addition, we demonstrate how existing electronic voting systems can be easily converted to blockchain-based electronic voting systems by applying a blockchain-linked REST API. This study greatly contributes to enabling electronic voting using blockchain technology through cost reductions, information restoration, prevention of misrepresentation, and transparency enhancement for a variety of different forms of voting.