Bryan Tan, Benjamin Mariano, Shuvendu K. Lahiri, Işıl Dillig · 5 authors
As smart contracts gain adoption in financial transactions, it becomes increasingly important to ensure that they are free of bugs and security vulnerabilities. Of particular relevance in this context are arithmetic overflow bugs, as integers are often used to represent financial assets like account balances. Motivated by this observation, this paper presents SolType, a refinement type system for Solidity that can be used to prevent arithmetic over- and under-flows in smart contracts. SolType allows developers to add refinement type annotations and uses them to prove that arithmetic operations do not lead to over- and under-flows. SolType incorporates a rich vocabulary of refinement terms that allow expressing relationships between integer values and aggregate properties of complex data structures. Furthermore, our implementation, called Solid, incorporates a type inference engine and can automatically infer useful type annotations, including non-trivial contract invariants. To evaluate the usefulness of our type system, we use Solid to prove arithmetic safety of a total of 120 smart contracts. When used in its fully automated mode (i.e., using Solid's type inference capabilities), Solid is able to eliminate 86.3% of redundant runtime checks used to guard against overflows. We also compare Solid against a state-of-the-art arithmetic safety verifier called VeriSmart and show that Solid has a significantly lower false positive rate, while being significantly faster in terms of verification time.
Hongjun Wu, Zhuo Zhang, Shangwen Wang, Lei Yan · 8 authors
Smart contracts with natural economic attributes have been widely and rapidly developed in various fields. However, the bugs and vulnerabilities in smart contracts have brought huge economic losses, which has strengthened people's attention to the security issues of smart contracts. The immutability of smart contracts makes people more willing to conduct security checks before deploying smart contracts. Nonetheless, existing smart contract vulnerability detection techniques are far away from enough: static analysis approaches rely heavily on manually crafted heuristics which is difficult to reuse across different types of vulnerabilities while deep learning based approaches also have unique limitations. In this study, we propose a novel approach, Peculiar, which uses Pre-training technique for detection of smart contract vulnerabilities based on crucial data flow graph. Compared against the traditional data flow graph which is already utilized in existing approach, crucial data flow graph is less complex and does not bring an unnecessarily deep hierarchy, which makes the model easy to focus on the critical features. Moreover, we also involve pre-training technique in our model due to the dramatic improvements it has achieved on a variety of NLP tasks. Our empirical results show that Peculiar can achieve 91.80 % precision and 92.40 % recall in detecting reentrancy vulnerability, one of the most severe and common smart contract vulnerabilities, on 40,932 smart contract files, which is significantly better than the state-of-the-art methods (e.g., Smartcheck achieves 79.37% precision and 70.50% recall). Meanwhile, another experiment shows that Peculiar is more discerning to reentrancy vulnerability than existing approaches. The ablation experiment reveals that both crucial data flow graph and pre-trained model contribute significantly to the performances of Peculiar.
Smart cameras, as typical IoT devices, are widely adopted to provide surveillance on individuals, homes, and the environment. The unavoidably captured sensitive visuals via these cameras may raise significant security concerns, while the prevalent software defects and authentication misconfiguration issues aggravate the vulnerability of such devices. However, traditional cryptography techniques are inadequate to provide full protection of these devices due to the large computation overhead. In this context, realizing accountability for these surveillance systems shall be the last line of defense in the presence of fast-evolving and high-influential threats. We propose EviChain, a scalable blockchain-based solution to trace the operations on intelligent surveillance cameras and reserve the evidence for any misuse in tamper-proofing manipulation records. Building a blockchain over the distributed cameras is challenging due to the limited capacity of on-board memory. To tackle this challenge, we design a cooperative mechanism that enables cameras to adaptively join in groups and share storage for recording blocks. In addition, we present a computation efficiency and delay-aware block generation strategy to reduce the cost of the consensus process. We perform extensive simulations to validate the superior performance of EviChain over other baselines, for example, Practical Byzantine Fault Tolerance (PBFT).
With the boom of Initial Coin Offerings (ICO) in the financial markets, smart contracts have gained rapid popularity among consumers. Smart contract vulnerabilities however made them a prime target to malicious attacks that are leading to huge losses. The research community is thus applying various software engineering technologies to smart contracts to address them. In general, to detect vulnerabilities in smart contracts, mutation and fuzz based testing approaches have been widely studied and indeed achieved promising performance on benchmark datasets. Generating test inputs with mutation approaches essentially relies on the available test cases in a smart contract program. In our preliminary study, however, we observed that 56.4% of 218 identified open-source smart contract project repositories do not provide any test case for validation. Fuzzing test inputs leads to random values and lacks practical usefulness. Our work addresses this problem: we propose an approach, Smartgift, which generates practical inputs for testing smart contracts by learning from the transaction records of real-world smart contracts. Leveraging a collected set of over 60 thousand transaction records, Smartgift is able to generate relevant test inputs for ~77% smart contract functions, largely outperforming the traditional fuzzing approach (successful for only 60% functions). We further demonstrate the practicality of the test inputs by using them to replace the test inputs of the ContractFuzzer state of the art smart contract vulnerability detector: with inputs by Smartgift, ContractFuzzer can now detect 131 of the 154 vulnerabilities in its benchmark.
Jinlei Sun, Song Huang, Changyou Zheng, Tingyong Wang · 6 authors
Integer overflow is a common vulnerability in Ethereum Smart Contracts (ESCs) and often causes huge economic losses. Smart contracts cannot be changed once it is deployed on the blockchain and thus demand further testing. Mutation testing is a fault-based testing method that can effectively improve the sufficiency of a test for smart contracts. However, existing methods cannot efficiently perform mutation testing specifically for integer overflow in ESCs. Therefore, by analyzing integer overflow in ESCs, we propose five special mutation operators to address such vulnerability in terms of detecting sufficiency in ESC testing. An empirical study on 40 open-source ESCs is conducted to evaluate the effectiveness of the proposed mutation operators. Results show that: (1) our proposed mutation operators can reproduce all 179 integer overflow vulnerabilities in 40 smart contracts, and the generated mutants have high compilation pass rate and integer overflow vulnerability generation rate; moreover, (2) the generated mutants can find the shortcomings of existing testing methods for integer overflow vulnerability, thereby providing effective support to improve the sufficiency of the test.
Yajing Wang, Jingsha He, Nafei Zhu, Yuzi Yi · 7 authors
Abstract Smart contract has shown its potential in cutting down the cost of administration through reshaping conventional business processes and in expanding the application of blockchain to areas that are beyond the cryptocurrency. However, with the rapid development and wide application of smart contracts, security issues have become a serious concern and have thus attracted widespread attention. As the result, a great deal of effort has been spent on improving and supporting secure development and on the application of smart contracts by introducing new and advanced vulnerability detection and privacy protection techniques in recent years. There is therefore the need for a comprehensive review of the new development on security enhancement technologies of smart contracts for the blockchain. This paper provides a review of the current research status and advances in smart contract security based on related literature published in recent years. Our review is divided into six categories along the line of the technology, which includes symbolic execution, abstract interpretation, fuzz testing, formal verification, deep learning, and privacy enhancement. A comparison of the various tools and methods developed to tackle security issues is provided. Challenges in the research of smart contract security as well as future directions are discussed. This paper intends to provide the inspiration and reference for follow‐up research on the security issues of smart contracts in the blockchain.
Ulrich Haböck, Alberto Garoffolo, Daniele Di Benedetto
This document describes Darlin, a succinct zero-knowledge argument of knowledge based on the Marlin SNARK (Chiesa et al., Eurocrypt 2020) and the `dlog' polynomial commitment scheme from Bootle et al. EUROCRYPT 2016. Darlin addresses recursive proofs by integrating the amortization technique from Halo (IACR eprint 2019/099) for the non-succinct parts of the dlog verifier, and we adapt their strategy for bivariate circuit encoding polynomials to aggregate Marlin's inner sumchecks across the nodes the recursive scheme. We estimate the performance impact of inner sumcheck aggregation by about 30% in a tree-like scheme of in-degree 2, and beyond when applied to linear recursion.
Security of smart contracts has attracted increasing attention in recent years. Many researchers have devoted themselves to devising testing tools for vulnerability detection. Each published tool has demonstrated its effectiveness through a series of evaluations on their own experimental scenarios. However, the inconsistency of evaluation settings such as different data sets or performance metrics, may result in biased conclusion.
Lukas Burkhalter, Hidde Lycklama, Alexander Viand, Nicolas Küchler · 5 authors
Federated Learning is an emerging decentralized machine learning paradigm that allows a large number of clients to train a joint model without the need to share their private data. Participants instead only share ephemeral updates necessary to train the model. To ensure the confidentiality of the client updates, Federated Learning systems employ secure aggregation; clients encrypt their gradient updates, and only the aggregated model is revealed to the server. Achieving this level of data protection, however, presents new challenges to the robustness of Federated Learning, i.e., the ability to tolerate failures and attacks. Unfortunately, in this setting, a malicious client can now easily exert influence on the model behavior without being detected. As Federated Learning is being deployed in practice in a range of sensitive applications, its robustness is growing in importance. In this paper, we take a step towards understanding and improving the robustness of secure Federated Learning. We start this paper with a systematic study that evaluates and analyzes existing attack vectors and discusses potential defenses and assesses their effectiveness. We then present RoFL, a secure Federated Learning system that improves robustness against malicious clients through input checks on the encrypted model updates. RoFL extends Federated Learning's secure aggregation protocol to allow expressing a variety of properties and constraints on model updates using zero-knowledge proofs. To enable RoFL to scale to typical Federated Learning settings, we introduce several ML and cryptographic optimizations specific to Federated Learning. We implement and evaluate a prototype of RoFL and show that realistic ML models can be trained in a reasonable time while improving robustness.
Machine learning has become ubiquitous across many fields in the last decade and modern real world applications often require a decentralized solution for training such models. This demand sprouted the research in federated learning, which solves some of the challenges with centralized machine learning, but at the same times raises further questions in regard to security, privacy and scalability. We have designed and implemented DecFL, an ubiquitous protocol for decentralized model training. The protocol is machine-learning-model-, vendor-, and technology-agnostic and provides a basis for practitioner's own implementations. The implemented DecFL framework presented in this article is an exemplary realization of the carefully designed protocol stack based on Ethereum and IPFS and offers a scalable baseline solution for decentralized machine learning. In this article, we present a study based on the proposed protocol, its theoretical bounds and experiments based on the implemented framework. Using open-source datasets (MNIST and CIFAR10), we demonstrate key features, the actual cost of training a model (in euro) and the communication overhead. We further show that through a proper choice of technologies DecFL achieves a linear scaling, which is a non-trivial task in a decentralized setting. Along with discussing some of the security challenges in the field, we highlight aggregation poisoning as a relevant attack vector, its associated risks and a possible prevention strategy for decentralized model training through DecFL.
Distributed multi-agent learning enables agents to cooperatively train a model without requiring to share their datasets. While this setting ensures some level of privacy, it has been shown that, even when data is not directly shared, the training process is vulnerable to privacy attacks including data reconstruction and model inversion attacks. Additionally, malicious agents that train on inverted labels or random data, may arbitrarily weaken the accuracy of the global model. This paper addresses these challenges and presents Privacy-preserving and Accountable Distributed Learning (PA-DL), a fully decentralized framework that relies on Differential Privacy to guarantee strong privacy protection of the agents data, and Ethereum smart contracts to ensure accountability.
With the increase of the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have become more popular to the point that billions of US Dollars are currently exchanged every day through such technology. Meanwhile, various vulnerabilities in smart contracts have been exploited by attackers to steal cryptocurrencies worth millions of dollars. The automatic detection of smart contract vulnerabilities therefore is an essential research problem. Existing solutions to this problem particularly rely on human experts to define features or different rules to detect vulnerabilities. However, this often causes many vulnerabilities to be ignored, and they are inefficient in detecting new vulnerabilities. In this study, to overcome such challenges, we propose the VSCL framework to automatically detect vulnerabilities in smart contracts on the blockchain. More specifically, first, we utilize novel feature vector generation techniques from bytecode of smart contract since the source code of smart contracts are rarely available in public. Next, the collected vectors are fed into our novel metric learning-based deep neural network(DNN) to get the detection result. We conduct comprehensive experiments on a large-scale benchmark, and the quantitative results demonstrate the effectiveness and efficiency of our approach.
In recent years, DNNs (Deep Neural Networks) have been applied into various fields and expected to be deployed into real-world applications. On the other hand, lack of transparency in DNNs makes them unreliable. To ensure transparency in DNN models, it's necessary that model validators can verify the entire learning process and convince third parties with limited resource that the given model is correctly generated. For this purpose, we propose a smart contract that is based on the dispute resolution protocol for verifying DNN model generation process. We divide the entire learning process into layer-based computations. The necessary data for validating each computation, such as the outputs of neurons, weights between layers and their gradients, are uniquely determined by the one-way hash function and the hashes are combined by multiple structured Merkle trees. This enables an honest validator to make a proof that asserts the target model is incorrectly generated, and third parties can check whether the assertion is true by only performing the given computation. Finally, to reveal how our proposal affects the performance degradation during the training, we evaluated running time for deep learning that enables our proofs. This result shows that our proposal can be applied into real applications. For this purpose, we propose a smart contract that is based on the dispute resolution protocol for verifying DNN model generation process. We divide the entire learning process into layer-based computations. The necessary data for validating each computation, such as the outputs of neurons, weights between layers and their gradients, are uniquely determined by the one-way hash function and the hashes are combined by multiple structured Merkle trees. This enables an honest validator to make a proof that asserts the target model is incorrectly generated, and third parties can check whether the assertion is true by only performing the given computation. Finally, to reveal how our proposal affects the performance degradation during the training, we evaluated running time for deep learning that enables our proofs. This result shows that our proposal can be applied into real applications. Finally, to reveal how our proposal affects the performance degradation during the training, we evaluated running time for deep learning that enables our proofs. This result shows that our proposal can be applied into real applications.
Stefan Driessen, Dario Di Nucci, Geert Monsieur, Damian A. Tamburri · 5 authors
Blockchain and smart contract technology are novel approaches to data and\ncode management that facilitate trusted computing by allowing for development\nin a distributed and decentralized manner. Testing smart contracts comes with\nits own set of challenges which have not yet been fully identified and\nexplored. Although existing tools can identify and discover known\nvulnerabilities and their interactions on the Ethereum blockchain through\nrandom search or symbolic execution, these tools generally do not produce test\nsuites suitable for human oracles. In this paper, we present AGSOLT (Automated\nGenerator of Solidity Test Suites). We demonstrate its efficiency by\nimplementing two search algorithms to automatically generate test suites for\nstand-alone Solidity smart contracts, taking into account some of the\nblockchain-specific challenges. To test AGSOLT, we compared a random search\nalgorithm and a genetic algorithm on a set of 36 real-world smart contracts. We\nfound that AGSOLT is capable of achieving high branch coverage with both\napproaches and even discovered some errors in some of the most popular Solidity\nsmart contracts on Github.\n
Rachit Agarwal, Tanmay Thapliyal, Sandeep K. Shukla
Different types of malicious activities have been flagged in multiple\npermissionless blockchains such as bitcoin, Ethereum etc. While some malicious\nactivities exploit vulnerabilities in the infrastructure of the blockchain,\nsome target its users through social engineering techniques. To address these\nproblems, we aim at automatically flagging blockchain accounts that originate\nsuch malicious exploitation of accounts of other participants. To that end, we\nidentify a robust supervised machine learning (ML) algorithm that is resistant\nto any bias induced by an over representation of certain malicious activity in\nthe available dataset, as well as is robust against adversarial attacks. We\nfind that most of the malicious activities reported thus far, for example, in\nEthereum blockchain ecosystem, behaves statistically similar. Further, the\npreviously used ML algorithms for identifying malicious accounts show bias\ntowards a particular malicious activity which is over-represented. In the\nsequel, we identify that Neural Networks (NN) holds up the best in the face of\nsuch bias inducing dataset at the same time being robust against certain\nadversarial attacks.\n
Abstract The past few years have witnessed the resurgence of uncertainty estimation generally in neural networks. Providing uncertainty quantification besides the predictive probability is desirable to reflect the degree of belief in the model’s decision about a given input. Recently, Monte-Carlo dropout (MC-dropout) method has been introduced as a probabilistic approach based Bayesian approximation which is computationally efficient than Bayesian neural networks. MC-dropout has revealed promising results on image datasets regarding uncertainty quantification. However, this method has been subjected to criticism regarding the behaviour of MC-dropout and what type of uncertainty it actually captures. For this purpose, we aim to discuss the behaviour of MC-dropout on classification tasks using synthetic and real data. We empirically explain different cases of MC-dropout that reflects the relative merits of this method. Our main finding is that MC-dropout captures datapoints lying on the decision boundary between the opposed classes using synthetic data. On the other hand, we apply MC-dropout method on dataset derived from Bitcoin known as Elliptic data to highlight the outperformance of model with MC-dropout over standard model. A conclusion and possible future directions are proposed.
Covert channels are designed to protect the communication relationship of the sender and receiver. Traditional covert channels have become insecure due to the continuous improvement of traffic analysis techniques. In this context, there is an urgent need to identify new approaches for covert channels. Blockchain is an emerging technique with characteristics of user anonymity, a flooding propagation mechanism, and tamper resistance, which make it a compelling platform for covert channels. Previous approaches applied Bitcoin as the underlying blockchain, and its pseudoanonymity may expose the communication relationship. Moreover, the reliance of these approaches on prenegotiated labels to identify transactions containing covert messages further reduced their concealment. In this work, we present a practical and secure covert channel over Monero. Compared to Bitcoin, Monero's full anonymity efficiently protects the relationship between the sender and receiver. Moreover, no labels are employed to identify special transactions. The receiver filters and extracts the covert message using his private key. In this study, we make a complete assessment of the robustness, reliability, and anti-traceability of our protocol, as these properties are regarded as desirable for a covert channel. We also formalize the definition of security for covert channels through a transaction distinguishing experiment. A rigorous proof shows that our protocol meets this definition and is secure to use. Finally, we make a detailed comparison between our protocol and the existing blockchain-based covert channels.
Open access
Internet Traffic Analysis and Secure E-voting
Adversarial Robustness in Machine Learning
Advanced Steganography and Watermarking Techniques
Low-rate Distributed DoS (LDDoS) attack is a complex large-scale attack behavior with strong time-domain characteristics in blockchain-based wireless network. Blockchain with Machine learning-based models, as promising ways, are taken to detect them and secure wireless network. However, researchers focused on how to improve models' detection performance and work out new blockchain-based protection technologies during the past decades. Due to lack of evolving data, these models and technologies may have poor stability in the face of confrontational samples. To cope with the problem, this paper proposes a novel LSTM-CGAN method to generate high-quality LDDoS adversarial samples for blockchain-based wireless network detection models. In this method, we give a brief feature analysis about LDDoS attack in blockchain-based wireless network and work out its corresponding time series model firstly. And then, we take use of Long Short-Term Memory Networks (LSTM) to learn relationships among sequenced network packages in the same flow. At last, we establish a Condition Generative Adversarial Networks (CGAN) model to use above relationships as specific conditions for generating mimicking behaviors of LDDoS attacks in blockchain-based wireless network. The experimental results show that these generated adversarial samples based on both public and private datasets can cheat the machine learning detection models, and have the similar attack characteristics with the real samples. Consequently, they can be used as blockchain-based wireless network dataset of machine learning classifiers for training to enhance models' stability.
Routing is a critical process in Wireless Sensor Networks (WSNs) since it is responsible for data transmission to base stations. Routing attacks are capable of completely destroying and degrading the function of WSNs. A trustworthy routing system is critical for ensuring routing security and WSN efficiency. Numerous studies have been conducted to increase trust between routing nodes, including cryptographic techniques, and centralized routing decisions. Nonetheless, the majority of routing methods are impractical in practice, since it is difficult to identify untrusted activities of routing nodes effectively. Meanwhile, there is no efficient method of preventing malicious node attacks. As a result of these issues, this article offers a trusted routing method that combines deep blockchain and Markov Decision Processes (MDPs) in order to enhance the routing security and efficiency of WSNs. To authenticate the process of transmitting the node, the proposed approach utilizes a Proof of Authority (PoA) method inside the blockchain network. The validation group necessary for proofing is selected using a deep learning methodology that focuses on the properties of each node. MDPs are then used to choose the appropriate next hop as a forwarding node capable of transferring messages simply and securely. According to testing data, our routing system still performs well in a 50% malicious node routing environment when compared to existing routing algorithms.