The widespread recognition of the smart contracts has established their importance in the landscape of next generation blockchain technology. However, writing a correct smart contract is notoriously difficult. Moreover, once a state-changing transaction is confirmed by the network, the result is immutable. For this reason, it is crucial to perform a thorough testing of a smart contract application before its deployment. This paper's focus is on the test coverage criteria for smart contracts, which are objective rules that measure test quality. We analyze the unique characteristics of the Ethereum smart contract program model as compared to the conventional program model. To capture essential control flow behaviors of smart contracts, we propose the notions of whole transaction basis path set and bounded transaction interaction. The former is a limited set of linearly independent inter-procedural paths from which the potentially infinite paths of Ethereum transactions can be constructed by linear combination, while the latter is the permutations of transactions within a certain bound. Based on these two notions, we define a family of path-based test coverage criteria. Algorithms are given to the generation of coverage requirements. A case study is conducted to compare the effectiveness of the proposed test coverage criteria with random testing and statement coverage testing.
William Zhang, Sebastian Banescu, Leonardo Pasos, Steven Stewart · 5 authors
Smart contracts are executable programs that enable the building of a programmable trust mechanism between multiple entities without the need of a trusted third-party. At the time of this writing, there were over 10 million smart contracts deployed on the Ethereum networks and this number continues to grow at a rapid pace. Smart contracts are often written in a Turing-complete programming language called Solidity, which is not easy to audit for subtle errors. Further, since smart contracts are immutable, errors have led to attacks resulting in losses of cryptocurrency worth 100s of millions of USD and reputational damage. Unfortunately, manual security analyses do not scale with size and number of smart contracts. Automated and scalable mechanisms are essential if smart contracts are to gain mainstream acceptance. Researchers have developed several security scanners in the past couple of years. However, many of these analyzer either do not scale well, or if they do, produce many false positives. This issue is exacerbated when bugs are triggered only after a series of interactions with the functions of the contract-under-test. A depth-n vulnerability, refers to a vulnerability that requires invoking a specific sequence of n functions to trigger. Depth-n vulnerabilities are time-consuming to detect by existing automated analyzers, because of the combinatorial explosion of sequences of functions that could be executed on smart contracts. In this paper, we present a technique to analyze depth-n vulnerabilities in an efficient and scalable way by combining symbolic execution and data dependency analysis. A significant advantage of combining symbolic with static analysis is that it scales much better than symbolic alone and does not have the problem of false positive that static analysis tools typically have. We have implemented our technique in a tool called MPro, a scalable and automated smart contract analyzer based on the existing symbolic analysis tool Mythril-Classic and the static analysis tool Slither. We analyzed 100 randomly chosen smart contracts on MPro and our evaluation shows that MPro is about n-times faster than Mythril-Classic for detecting depth-n vulnerabilities, while preserving all the detection capabilities of Mythril-Classic.
Virtual Currencies and cryptocurrency are a trending digital currency method which uses the Blockchain technology. Cryptocurrency is a digital method designed to exchange the asset between the users based on a powerful cryptography which ensures the transaction are safe and controllable. We have various legal areas identified while using the cryptocurrency, as being the virtual currency, the amount of assets used by the users increases rapidly. With the increase in the asset the security breaches are one of the key vulnerable areas to focus. Cryptocurrency mining malware or Cryptojacking remains a trending terminology which identifies the malicious software or malware developed to use the data from the smart phones and computers. The major threat of the Cryptojacking is cryptocurrency mining without user’s approval. This article implemented based on our CCEC Framework method published for Malware detection in SMS’s for the Smartphone users. The article explains about how the Malware detected using the CCEC Framework. Malwares created in various format so identifying the Malware takes time before which user assets remains vulnerable. So, the proposed method ensures we have a reduction in time by using various online data sources to identify the Cryptojacking malware.
The blockchain is a key technology that has been adopted in many application areas to increase security and reliability and to avoid the need for a central trusted authority. One of its essential underlying foundations are smart contracts, which are executable programs for managing data or assets on the blockchain. It is crucial that smart contracts are tested thoroughly due to their immutable nature and since even small bugs can lead to huge monetary losses. However, it is not enough to just test smart contracts, it is also important to ensure the quality and completeness of the tests. Hence, we introduce new smart contract specific mutation operators as well as a novel killing condition that is able to detect a deviation in the gas consumptions, i.e., in the monetary value that is required to perform transactions. Moreover, we establish a baseline for mutation testing of smart contracts by applying our method to a replay test suite and by testing about a thousand contracts.
It is crucial that smart contracts are tested thoroughly due to their immutable nature. Even small bugs in smart contracts can lead to huge monetary losses. However, testing is not enough; it is also important to ensure the quality and completeness of the tests. There are already several approaches that tackle this challenge with mutation testing, but their effectiveness is questionable since they only considered small contract samples. Hence, we evaluate the quality of smart contract mutation testing at scale. We choose the most promising of the existing (smart contract specific) mutation operators, analyse their effectiveness in terms of killability and highlight severe vulnerabilities that can be injected with the mutations. Moreover, we improve the existing mutation methods by introducing a novel killing condition that is able to detect a deviation in the gas consumption, i.e., in the monetary value that is required to perform transactions. This paper has a replication package at https://github.com/pieterhartel/Mutation-at-scale
Blockchain systems, such as Ethereum, use an approach called "metering" to assign a cost to smart contract execution, an approach which is designed to incentivise miners to operate the network and protect it against DoS attacks. In the past, the imperfections of Ethereum metering allowed several DoS attacks which were countered through modification of the metering mechanism. This paper presents a new DoS attack on Ethereum which systematically exploits its metering mechanism. We first replay and analyse several months of transactions, during which we discover a number of discrepancies in the metering model, such as significant inconsistencies in the pricing of the instructions. We further demonstrate that there is very little correlation between the execution cost and the utilised resources, such as CPU and memory. Based on these observations, we present a new type of DoS attack we call Resource Exhaustion Attack, which uses these imperfections to generate low-throughput contracts. To do this, we design a genetic algorithm that generates contracts with a throughput on average 200 times slower than typical contracts. We then show that all major Ethereum client implementations are vulnerable and, if running on commodity hardware, would be unable to stay in sync with the network when under attack. We argue that such an attack could be financially attractive not only for Ethereum competitors and speculators, but also for Ethereum miners. Finally, we discuss short-term and potential long-term fixes against such attacks. Our attack has been responsibly disclosed to the Ethereum Foundation and awarded a bug bounty reward of 5,000 USD.
Haijun Wang, Ye Liu, Yi Li, Shang‐Wei Lin · 7 authors
Despite the high stakes involved in smart contracts, they are often developed in an undisciplined manner, leaving the security and reliability of blockchain transactions at risk. In this article, we introduce ContraMaster—an oracle-supported dynamic exploit generation framework for smart contracts. Existing approaches mutate only single transactions; ContraMaster exceeds these by mutating the transaction sequences. ContraMaster uses data-flow, control-flow, and the dynamic contract state to guide its mutations. It then monitors the executions of target contract programs, and validates the results against a general-purpose semantic test oracle to discover vulnerabilities. Being a dynamic technique, it guarantees that each discovered vulnerability is a violation of the test oracle and is able to generate the attack script to exploit this vulnerability. In contrast to rule-based approaches, ContraMaster has not shown any false positives, and it easily generalizes to unknown types of vulnerabilities (e.g., logic errors). We evaluate ContraMaster on 218 vulnerable smart contracts. The experimental results confirm its practical applicability and advantages over the state-of-the-art techniques, and also reveal three new types of attacks.
In a fully interconnected world where even network-related services are becoming more dependent on software, the management of license agreements is critical for the business of any software vendor and communication provider. Building, managing and protecting the infrastructure to handle software license validation and scalability for the provider and, on the other hand, assessing the correct use of the software licenses for the vendor can become an expensive part of the relationship costs. We propose a novel approach for decentralized software licensing that leverages blockchain and smart contracts as fundamental enabling technologies. Our proposal guarantees a secure and inexpensive system with no central point of failure that can regulate the relations among untrusted parties. We describe the main design choices and present a prototype experimentation that demonstrates the benefits of the proposal in the context of virtualized network infrastructures.
Ui-Jun Baek, Se-Hyun Ji, Jee- Tae Park, Min‐Seob Lee · 6 authors
Since the inception of Bitcoin, the first cryptocurrency to implement blockchain technology, the cryptocurrency market has experienced significant growth.However, this growth has also brought about numerous vulnerabilities and attacks that pose a threat to the Bitcoin ecosystem.These attacks are not only focused on the Bitcoin network itself but also extend to the services that utilize it.Recent surveys have indicated the need to analyze and identify Distributed Denial of Service (DDoS) attacks, considering the interconnectedness between network-level data and service-level DDoS attacks within the Bitcoin system.Typically, the Bitcoin network is considered resilient against DDoS attacks due to the decentralized nature of its ledger.Nevertheless, there are potential vulnerabilities that could be exploited, such as message spoofing using the Transmission Control Protocol (TCP).Additionally, DDoS attacks often target services associated with Bitcoin usage rather than directly impacting the network's performance or stealing currency.Although these service-level attacks may not have an immediate impact, they can ultimately undermine the value of Bitcoin, leading to depreciation.The majority of DDoS attacks on Bitcoin-related services occur on exchanges and mining pools.Our approach involves evaluating experimental outcomes based on proposed metrics to establish a correlation between network-level data and service-level DDoS attacks in the Bitcoin system.By doing so, we aim to detect and analyze these attacks, thereby identifying potential associations.Furthermore, we posit that the methodology employed in this study could be applicable to other blockchain systems, extending its usefulness beyond the Bitcoin network.
Sunbeom So, Myungho Lee, Jisu Park, Heejo Lee · 5 authors
We present VERISMART, a highly precise verifier for ensuring arithmetic safety of Ethereum smart contracts. Writing safe smart contracts without unintended behavior is critically important because smart contracts are immutable and even a single flaw can cause huge financial damage. In particular, ensuring that arithmetic operations are safe is one of the most important and common security concerns of Ethereum smart contracts nowadays. In response, several safety analyzers have been proposed over the past few years, but state-of-the-art is still unsatisfactory; no existing tools achieve high precision and recall at the same time, inherently limited to producing annoying false alarms or missing critical bugs. By contrast, VERISMART aims for an uncompromising analyzer that performs exhaustive verification without compromising precision or scalability, thereby greatly reducing the burden of manually checking undiscovered or incorrectly-reported issues. To achieve this goal, we present a new domain-specific algorithm for verifying smart contracts, which is able to automatically discover and leverage transaction invariants that are essential for precisely analyzing smart contracts. Evaluation with real-world smart contracts shows that VERISMART can detect all arithmetic bugs with a negligible number of false alarms, far outperforming existing analyzers.
Ethereum has become a widely used platform to enable secure, Blockchain-based financial and business transactions. However, a major concern in Ethereum is the security of its smart contracts. Many identified bugs and vulnerabilities in smart contracts not only present challenges to maintenance of blockchain, but also lead to serious financial loses. There is a significant need to better assist developers in checking smart contracts and ensuring their reliability.In this paper, we propose a web service tool, named SmartEmbed, which can help Solidity developers to find repetitive contract code and clone-related bugs in smart contracts. Our tool is based on code embeddings and similarity checking techniques. By comparing the similarities among the code embedding vectors for existing solidity code in the Ethereum blockchain and known bugs, we are able to efficiently identify code clones and clone-related bugs for any solidity code given by users, which can help to improve the users' confidence in the reliability of their code. In addition to the uses by individual developers, SmartEmbed can also be applied to studies of smart contracts in a large scale. When applied to more than 22K solidity contracts collected from the Ethereum blockchain, we found that the clone ratio of solidity code is close to 90\%, much higher than traditional software, and 194 clone-related bugs can be identified efficiently and accurately based on our small bug database with a precision of 96\%. SmartEmbed can be accessed at \url{http://www.smartembed.net}. A demo video of SmartEmbed is at \url{https://youtu.be/o9ylyOpYFq8}
Purathani Praitheeshan, Lei Pan, Jiangshan Yu, Joseph K. Liu · 5 authors
Smart contracts are software programs featuring both traditional applications\nand distributed data storage on blockchains. Ethereum is a prominent blockchain\nplatform with the support of smart contracts. The smart contracts act as\nautonomous agents in critical decentralized applications and hold a significant\namount of cryptocurrency to perform trusted transactions and agreements.\nMillions of dollars as part of the assets held by the smart contracts were\nstolen or frozen through the notorious attacks just between 2016 and 2018, such\nas the DAO attack, Parity Multi-Sig Wallet attack, and the integer\nunderflow/overflow attacks. These attacks were caused by a combination of\ntechnical flaws in designing and implementing software codes. However, many\nmore vulnerabilities of less severity are to be discovered because of the\nscripting natures of the Solidity language and the non-updateable feature of\nblockchains. Hence, we surveyed 16 security vulnerabilities in smart contract\nprograms, and some vulnerabilities do not have a proper solution. This survey\naims to identify the key vulnerabilities in smart contracts on Ethereum in the\nperspectives of their internal mechanisms and software security\nvulnerabilities. By correlating 16 Ethereum vulnerabilities and 19 software\nsecurity issues, we predict that many attacks are yet to be exploited. And we\nhave explored many software tools to detect the security vulnerabilities of\nsmart contracts in terms of static analysis, dynamic analysis, and formal\nverification. This survey presents the security problems in smart contracts\ntogether with the available analysis tools and the detection methods. We also\ninvestigated the limitations of the tools or analysis methods with respect to\nthe identified security vulnerabilities of the smart contracts.\n
Huashan Chen, Marcus Pendleton, Laurent Njilla, Shouhuai Xu
The blockchain technology is believed by many to be a game changer in many application domains, especially financial applications. While the first generation of blockchain technology (i.e., Blockchain 1.0) is almost exclusively used for cryptocurrency purposes, the second generation (i.e., Blockchain 2.0), as represented by Ethereum, is an open and decentralized platform enabling a new paradigm of computing --- Decentralized Applications (DApps) running on top of blockchains. The rich applications and semantics of DApps inevitably introduce many security vulnerabilities, which have no counterparts in pure cryptocurrency systems like Bitcoin. Since Ethereum is a new, yet complex, system, it is imperative to have a systematic and comprehensive understanding on its security from a holistic perspective, which is unavailable. To the best of our knowledge, the present survey, which can also be used as a tutorial, fills this void. In particular, we systematize three aspects of Ethereum systems security: vulnerabilities, attacks, and defenses. We draw insights into, among other things, vulnerability root causes, attack consequences, and defense capabilities, which shed light on future research directions.
Smart contract is a special program that manages digital assets on blockchain. It is difficult to recover the loss if users make transactions through buggy smart contracts, which cannot be directly fixed. Hence, it is important to ensure the correctness of smart contracts before deploying them. This paper proposes a systematic framework to mutation testing for smart contracts on Ethereum, which is currently the most popular open blockchain for deploying and running smart contracts. Fifteen novel mutation operators have been designed for Ethereum Smart Contracts (ESC), in terms of keyword, global variable/function, variable unit, and error handling. An empirical study on 26 smart contracts in four Ethereum DApps has been conducted to evaluate the effectiveness of mutation testing. The experimental results show that our approach can outperform the coverage-based approach on defect detection rate (96.01% vs. 55.68%). The ESC mutation operators are effective to reveal real defects and we found 117 out of 729 real bug reports are related to our operators. These show the great potential of using mutation testing for quality assurance of ESC.
Gustavo Betarte, Maximiliano Cristiá, Carlos Luna, Adrián Silveira · 5 authors
Emin Gün Sirer once said: It's clear that writing a robust, secure smart contract requires extreme amounts of diligence. It's more similar to writing code for a nuclear power reactor, than to writing loose web code [...] Yet the current Solidity language and underlying EVM seems designed more for the latter. Formal methods (FM) are mathematics-based software development methods aimed at producing "code for a nuclear power reactor". That is, due application of FM can produce bug-free, zero-defect, correct-by-construction, guaranteed, certified software. However, the software industry seldom use FM. One of the main reasons for such a situation is that there exists the perception (which might well be a fact) that FM increase software costs. On the other hand, FM can be partially applied thus producing high-quality software, although not necessarily bug-free. In this paper we outline some FM related techniques whose application the cryptocurrency community should take into consideration because they could bridge the gap between "loose web code" and "code for a nuclear power reactor".
The scale of criminal networks (e.g. drug syndicates and terrorist networks) extends globally and poses national security threat to many nations as they also tend to be technologically advance (e.g. Dark Web and Silk Road cryptocurrency). Therefore, it is critical for law enforcement agencies to be equipped with the latest tools in criminal network analysis (CNA) to obtain key hidden links (relationships) within criminal networks to preempt and disrupt criminal network structures and activities. Current hidden or missing link predictive models that are based on Social Network Analysis models rely on ML techniques to improve the performance of the models in terms of predictive accuracy and computing power. Given the improvement in the recent performance of Deep Reinforcement Learning (DRL) techniques which could train ML models through self-generated dataset, DRL can be usefully applied to domains with relatively smaller dataset such as criminal networks. The objective of this study is to assess the comparative performance of a CNA hidden link prediction model developed using DRL techniques against classical ML models such as gradient boosting machine (GBM), random forest (RF) and support vector machine (SVM). The experiment results exhibit an improvement in the performance of the DRL model of about 7.4% over the next best performing classical RF model trained within 1500 iterations. The performance of these link prediction models can be scaled up with the parallel processing capabilities of graphical processing units (GPUs), to significantly improve the speed of training the model and the prediction of hidden links.
The decentralized nature of blockchain technologies can well match the needs of integrity and provenances of evidences collecting in digital forensics (DF) across jurisdictional borders. In this paper, a novel blockchain-based DF investigation framework in the Internet of Things (IoT) and social systems environment is proposed, which can provide proof of existence and privacy preservation for evidence items examination. To implement such features, we present a block-enabled forensics framework for IoT, namely, IoT forensic chain (IoTFC), which can offer forensic investigation with good authenticity, immutability, traceability, resilience, and distributed trust between evidential entitles as well as examiners. The IoTFC can deliver a guarantee of traceability and track provenance of evidence items. Details of evidence identification, preservation, analysis, and presentation will be recorded in chains of block. The IoTFC can increase trust of both evidence items and examiners by providing transparency of the audit train. The use case demonstrated the effectiveness of the proposed method.
Seonghyeon Gong, Erzhena Tcydenova, Jeong Hoon Jo, Younghun Lee · 5 authors
The broadly configured smart city network requires a variety of security considerations for a heterogeneous device environment. Because a network of heterogeneous devices facilitates an attacker’s intrusion through a specific device or node, a device management framework is required to manage each node comprehensively. This paper proposes a blockchain-based device management framework for efficient device management, scalable firmware update and resiliences on attacks against smart city network. This framework offers four device management and firmware update mechanisms based on the performance and requirements of each device: bidirectional mechanism of general end node and a unidirectional mechanism of the lightweight end node. This difference optimizes the resource of network and devices in terms of management and security. All management history of each device is stored in the blockchain and transmitting firmware between vendor and management node is conducted through a smart contract of blockchain for security and resilience on the attack. Through the framework proposed in this paper, the confidentiality and availability of device management on smart city network as well as integrity, auditability, adaptability and authentication for each node are ensured and the effectiveness of the proposed framework is presented through the security analysis.
Ruffin White, Gianluca Caiazza, Agostino Cortesi, Young Im Cho · 5 authors
Event data recording is crucial in robotics research, providing prolonged insights into a robot's situational understanding, progression of behavioral state, and resulting outcomes. Such recordings are invaluable when debugging complex robotic applications or profiling experiments ex post facto. As robotic developments mature into production, both the roles and requirements of event logging will broaden, to include serving as evidence for auditors and regulators investigating accidents or fraud. Given the growing number of high profile public incidents involving self-driving automotives resulting in fatality and regulatory policy making, it is paramount that the integrity, authenticity and non-repudiation of such event logs are maintained to ensure accountability. Being mobile cyber-physical systems, robots present new threats, and vulnerabilities beyond traditional IT: unsupervised physical system access or postmortem collusion between robot and OEM could result in the truncation or alteration of prior records. In this letter, we address immutablization of log records via integrity proofs and distributed ledgers with special considerations for mobile and public service robot deployments.
Mark Mossberg, Felipe Andrés Manzano, Eric Hennenfent, Alex Groce · 8 authors
An effective way to maximize code coverage in software tests is through dynamic symbolic execution$-$a technique that uses constraint solving to systematically explore a program's state space. We introduce an open-source dynamic symbolic execution framework called Manticore for analyzing binaries and Ethereum smart contracts. Manticore's flexible architecture allows it to support both traditional and exotic execution environments, and its API allows users to customize their analysis. Here, we discuss Manticore's architecture and demonstrate the capabilities we have used to find bugs and verify the correctness of code for our commercial clients.
Determining the trust of an individual Bitcoin wallet is a difficult problem. There are no ratings, that offer vendors or exchanges meaningful information about the level of the taint of Bitcoins they are receiving. Lack of such information places exchanges liable in an event when the received Bitcoins are stolen or ill-gotten. In this paper, we try to solve this problem by introducing a Bitcoin address taint score called TaintRank. It provides insight into a specific wallet by taking the addresses it interacted with throughout history into consideration. This ranking method provides such Bitcoin exchange companies insight with whom they are trading.
Patrick L. Chapman, Dianxiang Xu, Lin Deng, Yin Xiong
Blockchain in recent years has exploded in popularity with Ethereum being one of the leading blockchain platforms. Solidity is a widely used scripting language for creating smart contracts in Ethereum applications. Quality assurance in Solidity contracts is of critical importance because bugs or vulnerabilities can lead to a considerable loss of financial assets. However, it is unclear what level of quality assurance is provided in many of these applications. Mutation testing is the process of intentionally injecting faults into a target program and then running the provided test suite against the various injected faults. Mutation testing is used to evaluate the effectiveness of a test suite, measuring the test suite’s capability of covering certain types of faults. This thesis presents Deviant, the first implementation of a mutation testing tool for Solidity smart contracts. Deviant implements mutation operators that cover the unique features of Solidity according to our constructed fault model, in addition to traditional mutation operators that exist for other programming languages. Deviant has been applied to five open-source Solidity projects: MetaCoin [30], MultiSigWallet [31], Alice [29], aragonOS [32], and OpenZeppelin [33]. Experimental results show that the provided test suites result in low mutation scores. These results indicate that the provided tests cannot ensure high-level assurance of code quality. Such evaluation results offer important guidelines for Solidity developers to implement more effective tests in order to deliver trustworthy code and reduce the risk of financial loss.
Ransomware represents a class of malicious applications that encrypts the files of infected system and demands from victims a payment in cryptocurrency in order to receive the decryption key. The mainstream adoption of cryptocurrencies increased the number of ransomware attack. The outbreaks had risen in complexity and received mass-media attention in 2017 when two destructive campaigns crippled companies and institutions around the world. These outbreaks continue at an accelerated pace even though efforts are made to improve the detection and mitigation of ransomware. The purpose of this research is to assess the efficiency of current malware analysis methods and technologies in the detection of ransomware. The experiments presented here were performed using antivirus engines and dynamic malware analysis against live obfuscated ransomware samples.