Due to the rapid development of blockchain technology in recent years, smart contracts have been widely applied in critical fields such as finance, insurance, healthcare, and the Internet of Things. However, smart contracts face increasingly serious security issues due to their unique operating environment and programming characteristics. We focus on Ethereum-based smart contracts and propose a high-precision and versatile detection method to address the integer overflow vulnerability, which significantly affects smart contract development and execution. Our method can also solve the problem of possible data shortage. Specifically, we utilize code embedding algorithms to convert Solidity-compiled smart contracts into spatial vectors, thereby retaining as much syntax and semantic information as possible. Based on this, we use Generative Adversarial Network (GAN) to train a small sample vector dataset to generate a substantial number of synthetic datasets. Our proposed model combines GAN discriminator feedback and vector similarity analysis to identify smart contracts that contain integer overflow vulnerabilities.
A non-interactive ZK (NIZK) proof enables verification of NP statements without revealing secrets about them. However, an adversary that obtains a NIZK proof may be able to clone this proof and distribute arbitrarily many copies of it to various entities: this is inevitable for any proof that takes the form of a classical string. In this paper, we ask whether it is possible to rely on quantum information in order to build NIZK proof systems that are impossible to clone. We define and construct unclonable non-interactive zero-knowledge arguments (of knowledge) for NP, addressing a question first posed by Aaronson (CCC 2009). Besides satisfying the zero-knowledge and argument of knowledge properties, these proofs additionally satisfy unclonability. Very roughly, this ensures that no adversary can split an honestly generated proof of membership of an instance $x$ in an NP language $\mathcal{L}$ and distribute copies to multiple entities that all obtain accepting proofs of membership of $x$ in $\mathcal{L}$. Our result has applications to unclonable signatures of knowledge, which we define and construct in this work; these non-interactively prevent replay attacks.
Open access
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Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
Mengliang Li, Xiaoxue Ren, Fu Han, Zhuo Li · 5 authors
Smart contracts are essential for executing computing logic on blockchain networks. However, they are also susceptible to various vulnerabilities. In recent years, the detection of smart contract vulnerabilities has become a significant concern due to the substantial losses caused by hacker attacks. Traditional vulnerability detection approaches rely on expert rules, which often suffer from limitations in accuracy and completeness. Deep learning-based methods offer better coverage of vulnerabilities but may overlook certain vulnerability characteristics and suffer from overfitting during training. In this paper, we propose a novel approach called ConvMHSA-SCVD, which combines knowledge-driven and data-driven algorithms together to detect smart contract vulnerabilities. By incorporating feature selection, data balancing, and a combination of multi-channel convolution and multi-head self-attention neural networks, our ConvMHSA-SCVD achieves effective vulnerability detection in smart contracts. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method in accuracy and F1 score, with improvements ranging from 0.4% to 3.84% and 1.28% to 1.90%, respectively.
Reentrancy vulnerabilities in Ethereum smart contracts have caused huge financial losses in recent years, promoting the development of reentrancy detection tools. Evaluation of reentrancy detection tools has emerged as an essential research focus consequently. However, despite numerous reentrancy detection evaluation approaches, there is no systematically classified set of reentrancy vulnerabilities in evaluation. This oversight may result in a limited understanding of the actual effectiveness of reentrancy detection tools. This paper employs mutation testing to generate classified sets of reentrancy vulnerabilities and evaluate reentrancy detection tools. To determine the types of reentrancy that can be mutated, we use symbolic execution to identify potential reentrancy paths in smart contracts. For each reentrancy type, we design one to three mutation operators. Then, we employ pattern matching to locate statements where mutation operators can be applied, thus generating sets of reentrancy vulnerabilities of different types. We implemented and compared our mutation testing tool with other tools. Results show that we generated at least 29% more classified reentrancy mutants. Furthermore, we evaluated two state-of-the-art reentrancy detection tools based on the classified reentrancy mutants. The findings provide directions for advancing reentrancy detection tools.
Anusha Iyer, Chloe Lee, T. Uday Kiran Reddy, Cyrus Rosenberg · 6 authors
With the rise in prominence of crowdsourced datasets in machine learning, data poisoning attacks pose a considerable threat. Many current defenses fall short because they are overly specialized for certain attacks, lack contribution incentives, and are difficult to integrate into current platforms. This paper explores the underaddressed system security problem posed by data poisoning through SeBRUS, a comprehensive data contribution application that leverages Ethereum smart contracts to secure crowdsourced datasets. SeBRUS introduces a voting network and poisoned data detection model, allowing for easy implementation with current platforms to defend against label-flipping, clean-label, and backdoor attacks.
Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks. Existing defense mechanisms face critical limitations in deployments, such as relying on impractical assumptions (e.g., adversaries acknowledging the presence of attacks before attacking) or undermining accuracy in model training, even in benign scenarios. To address these challenges, we propose CustodianFL, a two-staged anomaly detection method specifically designed for FL deployments. In the first stage, it flags suspicious client activities. In the second stage that is activated only when needed, it further examines these candidates using Three-Sigma Rule to identify and exclude truly malicious local models from FL training. To ensure integrity and transparency within the FL system, CustodianFL integrates zero-knowledge proofs, enabling clients to cryptographically verify the server's detection process without relying on the server's goodwill. CustodianFL operates without unrealistic assumptions and avoids interfering with FL training in attack-free scenarios. It bridges the gap between theoretical advances in FL security and the practical demands of real FL systems. Experimental results demonstrate that CustodianFL consistently delivers performance comparable to benign cases, highlighting its effectiveness in identifying and eliminating malicious models with high accuracy.
Zhipeng Wang, Nanqing Dong, Jiahao Sun, William J. Knottenbelt · 5 authors
Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator. FL can be a scalable machine learning solution in big data scenarios. Traditional FL relies on the trust assumption of the central aggregator, which forms cohorts of clients honestly. However, a malicious aggregator, in reality, could abandon and replace the client's training models, or insert fake clients, to manipulate the final training results. In this work, we introduce zkFL, which leverages zero-knowledge proofs to tackle the issue of a malicious aggregator during the training model aggregation process. To guarantee the correct aggregation results, the aggregator provides a proof per round, demonstrating to the clients that the aggregator executes the intended behavior faithfully. To further reduce the verification cost of clients, we use blockchain to handle the proof in a zero-knowledge way, where miners (i.e., the participants validating and maintaining the blockchain data) can verify the proof without knowing the clients' local and aggregated models. The theoretical analysis and empirical results show that zkFL achieves better security and privacy than traditional FL, without modifying the underlying FL network structure or heavily compromising the training speed.
Smart contracts manage a large number of digital assets which is attractive to attackers. There have been many attacks that have caused huge financial losses. Therefore, it is of great importance to detect vulnerabilities in smart contracts. Fuzzing is considered a promising approach to test smart contracts. However, the complexity of changing state variables and the handling of external parameters during mutation pose critical technical challenges for current smart contract fuzzers, hindering their ability to cover branches under complex constraints and leaving potential vulnerabilities for attackers to exploit. To tackle these problems, we design a guided mutation strategy combined with two novel techniques: Dynamic Dependency Learning (DDL) and Dynamic Variables Analysis (DVA). DDL learns the dependencies of sequences to provide guided transaction sequence generation for handling state variables in complex constraints, while DVA leverages variable-level dynamic taint analysis to process the external parameters and guide the mutation. We implement the proposed strategy on a fuzzer, called SeqFuzz. The experimental results show that SeqFuzz could cover more branches and detect more bugs in real-world smart contracts compared with state-of-the-art tools.
Kar Balan, Andrew Gilbert, Alexander Black, Simon Jenni · 6 authors
We present DECORAIT; a decentralized registry through which content creators may assert their right to opt in or out of AI training as well as receive reward for their contributions. Generative AI (GenAI) enables images to be synthesized using AI models trained on vast amounts of data scraped from public sources. Model and content creators who may wish to share their work openly without sanctioning its use for training are thus presented with a data governance challenge. Further, establishing the provenance of GenAI training data is important to creatives to ensure fair recognition and reward for their such use. We report a prototype of DECORAIT, which explores hierarchical clustering and a combination of on/off-chain storage to create a scalable decentralized registry to trace the provenance of GenAI training data in order to determine training consent and reward creatives who contribute that data. DECORAIT combines distributed ledger technology (DLT) with visual fingerprinting, leveraging the emerging C2PA (Coalition for Content Provenance and Authenticity) standard to create a secure, open registry through which creatives may express consent and data ownership for GenAI.
This paper serves as a progress report on our research, specifically focusing on utilizing interval analysis, an existing static analysis method, for detecting vulnerabilities in smart contracts. We present a selection of motivating examples featuring vulnerable smart contracts and share the results from our experiments conducted with various existing detection tools. Our findings reveal that these tools were unable to detect the vulnerabilities in our examples. To enhance detection capabilities, we implement interval analysis on top of Slither [3], an existing detection tool, and demonstrate its effectiveness in identifying certain vulnerabilities that other tools fail to detect.
Abstract Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks for their everyday functions on the road so that safety of passengers and vehicles can be ensured. This article presents a holistic review of cybersecurity attacks on sensors and threats regarding multi‐modal sensor fusion. A comprehensive review of cyberattacks on intra‐vehicle and inter‐vehicle communications is presented afterward. Besides the analysis of conventional cybersecurity threats and countermeasures for CAV systems, a detailed review of modern machine learning, federated learning, and blockchain approach is also conducted to safeguard CAVs. Machine learning and data mining‐aided intrusion detection systems and other countermeasures dealing with these challenges are elaborated at the end of the related section. In the last section, research challenges and future directions are identified. This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Technologies > Machine Learning Technologies > Internet of Things
Fair machine learning is a thriving and vibrant research topic. In this paper, we propose Fairness as a Service (FaaS), a secure, verifiable and privacy-preserving protocol to computes and verify the fairness of any machine learning (ML) model. In the deisgn of FaaS, the data and outcomes are represented through cryptograms to ensure privacy. Also, zero knowledge proofs guarantee the well-formedness of the cryptograms and underlying data. FaaS is model--agnostic and can support various fairness metrics; hence, it can be used as a service to audit the fairness of any ML model. Our solution requires no trusted third party or private channels for the computation of the fairness metric. The security guarantees and commitments are implemented in a way that every step is securely transparent and verifiable from the start to the end of the process. The cryptograms of all input data are publicly available for everyone, e.g., auditors, social activists and experts, to verify the correctness of the process. We implemented FaaS to investigate performance and demonstrate the successful use of FaaS for a publicly available data set with thousands of entries.
Federated learning has emerged as a promising technique for the Internet of Things (IoT) in various domains, including supply chain management. It enables IoT devices to collaboratively learn without exposing their raw data, ensuring data privacy. However, federated learning faces the threats of local data tampering and upload process attacks. This paper proposes an innovative framework that leverages Trusted Execution Environment (TEE) and blockchain technology to address the data security and privacy challenges in federated learning for IoT supply chain management. Our framework achieves the security of local data computation and the tampering resistance of data update uploads using TEE and the blockchain. We adopt Intel Software Guard Extensions (SGXs) as the specific implementation of TEE, which can guarantee the secure execution of local models on SGX-enabled processors. We also use consortium blockchain technology to build a verification network and consensus mechanism, ensuring the security and tamper resistance of the data upload and aggregation process. Finally, each cluster can obtain the aggregated parameters from the blockchain. To evaluate the performance of our proposed framework, we conducted several experiments with different numbers of participants and different datasets and validated the effectiveness of our scheme. We tested the final global model obtained from federated training on a test dataset and found that increasing both the number of iterations and the number of participants improves its accuracy. For instance, it reaches 94% accuracy with one participant and five iterations and 98.5% accuracy with ten participants and thirty iterations.
Jim Woodcock, Mikkel Schmidt Andersen, Diego F. Aranha, Stefan Hallerstede · 11 authors
This report describes the state of the art in verifiable computation. The problem being solved is the following: The Verifiable Computation Problem (Verifiable Computing Problem) Suppose we have two computing agents. The first agent is the verifier, and the second agent is the prover. The verifier wants the prover to perform a computation. The verifier sends a description of the computation to the prover. Once the prover has completed the task, the prover returns the output to the verifier. The output will contain proof. The verifier can use this proof to check if the prover computed the output correctly. The check is not required to verify the algorithm used in the computation. Instead, it is a check that the prover computed the output using the computation specified by the verifier. The effort required for the check should be much less than that required to perform the computation. This state-of-the-art report surveys 128 papers from the literature comprising more than 4,000 pages. Other papers and books were surveyed but were omitted. The papers surveyed were overwhelmingly mathematical. We have summarised the major concepts that form the foundations for verifiable computation. The report contains two main sections. The first, larger section covers the theoretical foundations for probabilistically checkable and zero-knowledge proofs. The second section contains a description of the current practice in verifiable computation. Two further reports will cover (i) military applications of verifiable computation and (ii) a collection of technical demonstrators. The first of these is intended to be read by those who want to know what applications are enabled by the current state of the art in verifiable computation. The second is for those who want to see practical tools and conduct experiments themselves.
Smart contracts, which provide user-defined logic to blockchains, have become increasingly popular in recent years due to their decentralized system architecture. They are executable programs that can automate transactions on the ethereum blockchain. However, security concerns with certain aspects of smart contracts can be challenging to address. Therefore, this paper proposed a multi-objective approach using a neural network makes it a more scalable and effective tool for detecting smart contract vulnerabilities than traditional approaches. With the increasing complexity of smart contracts and the growing importance of security in the blockchain space, a technique like multi-objective is becoming increasingly necessary to ensure the safety and reliability of decentralized applications. The proposed approach evaluated over 11000 real word ethereum smart contracts and detected two vulnerabilities without expert knowledge. The results showed that it achieved an average F1-score of 86.7 and 84.4 percent for reentrancy and timestamp vulnerability, respectively, indicating that the proposed approach has an impressive level of accuracy in classifying complex smart contracts, which has significant implications in the blockchain security field.
Dongcheng Li, W. Eric Wong, Sean Pan, Liang-Seng Koh · 5 authors
Blockchain and smart contract technologies have emerged as innovative approaches to trustworthy and reliable service computing. With the growing popularity of decentralized applications, the quantity of smart contracts has seen an exponential increase. Research on testing smart contracts has primarily focused on identifying specific vulnerabilities in smart contracts and blockchains. However, generating a robust test suite for smart contracts remains a daunting challenge. The state-of-the-art DynaMOSA algorithm uses many-objective optimization for test case generation, introducing preference sorting and dynamic target selection strategies. Yet, under hyper many-objective conditions, the algorithm faces difficulties including insufficient selection pressure and reduced efficiency. This paper proposes a solution to these issues by introducing a many-objective optimization algorithm with a dimensionality reduction strategy. The goal is to condense an extensive dataset into a smaller, more manageable and effective dataset using specific reduction criteria. Additionally, this paper utilizes 28 open-source Solidity projects from GitHub for testing. The experimental results show that compared to DynaMOSA, the proposed approach achieves higher testing coverage on most projects under test and shows a noticeable improvement in algorithm efficiency for the automatic generation of Solidity Tests.
Yuqiang Sun, Daoyuan Wu, Yue Xue, Han Liu · 8 authors
Smart contracts are prone to various vulnerabilities, leading to substantial financial losses over time. Current analysis tools mainly target vulnerabilities with fixed control or data-flow patterns, such as re-entrancy and integer overflow. However, a recent study on Web3 security bugs revealed that about 80% of these bugs cannot be audited by existing tools due to the lack of domain-specific property description and checking. Given recent advances in Large Language Models (LLMs), it is worth exploring how Generative Pre-training Transformer (GPT) could aid in detecting logicc vulnerabilities. In this paper, we propose GPTScan, the first tool combining GPT with static analysis for smart contract logic vulnerability detection. Instead of relying solely on GPT to identify vulnerabilities, which can lead to high false positives and is limited by GPT's pre-trained knowledge, we utilize GPT as a versatile code understanding tool. By breaking down each logic vulnerability type into scenarios and properties, GPTScan matches candidate vulnerabilities with GPT. To enhance accuracy, GPTScan further instructs GPT to intelligently recognize key variables and statements, which are then validated by static confirmation. Evaluation on diverse datasets with around 400 contract projects and 3K Solidity files shows that GPTScan achieves high precision (over 90%) for token contracts and acceptable precision (57.14%) for large projects like Web3Bugs. It effectively detects ground-truth logic vulnerabilities with a recall of over 70%, including 9 new vulnerabilities missed by human auditors. GPTScan is fast and cost-effective, taking an average of 14.39 seconds and 0.01 USD to scan per thousand lines of Solidity code. Moreover, static confirmation helps GPTScan reduce two-thirds of false positives.
Smart contracts are one of the most successful applications of blockchain technology. In order to guarantee the security of smart contracts, researchers have successively introduced various testing methodologies, including static analysis, symbolic execution, and fuzzing, which contribute to a more rigorous and precise evaluation of smart contract vulnerabilities. Deep learning techniques have been widely applied in traditional software vulnerability detection, while the opposite is true in the field of smart contract testing. Consequently, we anticipate that deep learning can be similarly applied to enhance traditional smart contract vulnerability detection tools. However, there is a lack of empirical study on the performance of deep learning applied to smart contract testing. In order to explore how deep neural networks can help with testing tools on smart contracts, we construct a test framework based on SMARTEST. We manage to train deep learning language models using various neural networks including Transformer, GRU, RNN and test the symbolic execution tool SMARTEST framework with the application of these models on the CVE dataset. Upon analyzing the experimental results, we find that deep neural networks did not surpass traditional language models in enhancing smart contract testing. In terms of accuracy, the SMARTEST tool, which utilizes a statistical 3-gram language model, succeeded in detecting the greatest number of vulnerabilities. Specifically, the 3-gram model was able to identify 69.8% of vulnerabilities in the benchmark set within the first 5 seconds. Based on our experimental findings and thorough analysis, we outline the challenges faced in DNN-assisted smart contract testing and suggest potential directions for improvement.
Recently, the intellectual property protection methods based on deep learning have achieved great success, but there are still serious infringement issues that the network topology or hyper parameters of the trained model are stolen by third parties. In this paper, we construct a deep learning model based on the autoencoder to remove the bone from the medical images containing chest x-ray, and the specific trigger set is trained and predicted to get the effect of the backdoor watermark. The scheme of zero-knowledge proof is applied to transform the backdoor watermark of the model into the fixed-length string, which is published in the block chain to verify the ownership of the model. Through the non-interactive verification between the model owner and the third party, the ownership of the model can be confirmed by the third party and the verification process will not disclose any information of the model itself. The method proposed in this paper can support infinite times of verification and does not reveal any information about the model, so as to achieve the protection of intellectual property rights of the model.
Adversarial Robustness in Machine Learning
Generative Adversarial Networks and Image Synthesis
Haochen Sun, Tonghe Bai, J. Li, Change Institutions to: University of Waterloo
The recent advancements in deep learning have brought about significant changes in various aspects of people’s lives. Meanwhile, these rapid developments have raised concerns about the legitimacy of the training process of deep neural networks. To protect the intellectual properties of AI developers, directly examining the training process by accessing the model parameters and training data is often prohibited for verifiers. In response to this challenge, we present zero-knowledge deep learning (zkDL), an efficient zero-knowledge proof for deep learning training. To address the long-standing challenge of verifiable computations of non-linearities in deep learning training, we introduce zkReLU, a specialized proof for the ReLU activation and its backpropagation. zkReLU turns the disadvantage of non-arithmetic relations into an advantage, leading to the creation of FAC4DNN, our specialized arithmetic circuit design for modelling neural networks. This design aggregates the proofs over different layers and training steps, without being constrained by their sequential order in the training process. With our new CUDA implementation that achieves full compatibility with the tensor structures and the aggregated proof design, zkDL enables the generation of complete and sound proofs in less than a second per batch update for an 8-layer neural network with 10M parameters and a batch size of 64, while provably ensuring the privacy of data and model parameters. To our best knowledge, we are not aware of any existing work on zero-knowledge proof of deep learning training that is scalable to million-size networks.
Pengcheng Zhang, Qifan Yu, Yan Xiao, Hai Dong · 7 authors
With the rising prominence of smart contracts, security attacks targeting them have increased, posing severe threats to their security and intellectual property rights. Existing simplistic datasets hinder effective vulnerability detection, raising security concerns. To address these challenges, we proposeBiAn, a source code level smart contract obfuscation method that generates complex vulnerability test datasets.BiAnprotects contracts by obfuscating data flows, control flows, and code layouts, increasing complexity and making it harder for attackers to discover vulnerabilities. Our experiments with buggy contracts showed an average complexity enhancement of approximately 174% after obfuscation. Decompilers Vandal and Gigahorse had total failure rate increments of 38.8% and 40.5% respectively. Obfuscated contracts also decreased vulnerability detection rates in more than 50% of cases for ten widely-used static analysis detection tools.
Blockchain and Artificial Intelligence (AI) are two rapidly emerging technologies. The intersection between them typically focuses on areas where the blockchain can improve trust and transparency in the decisions made by AI models. Yet few practical use cases exist that demonstrate where AI can be used to benefit blockchain. In this paper, we identify Non-Fungible Tokens (NFTs) as a concrete example of how techniques developed in AI can enhance the functionality of blockchain systems and enable new use cases. Specifically, we explore how singular value decomposition (SVD), a fundamental tool in the field of machine learning, can be used in several ways to generate composite NFTs. We show how using the SVD to generate NFTs can allow their more efficient storage on public blockchains as low-fidelity thumbnails that can be provably linked to the original full image. Building on this design, we outline how composite NFTs can be constructed by combining different components of an NFT image derived using the SVD. These components can either be made public or kept private to allow for complex functionality in novel NFT protocols.
Blockchain technology has attracted significant attention in recent years. One of the reasons for this new trend is the introduction of on-chain smart contracts, enabling the implementation of decentralized applications in trustless environments. Given the high amounts handled, smart contracts have become the targets of attacks. Along with its adoption, attacks are perpetrated via criminal smart contracts and vulnerable smart contracts by exploiting vulnerabilities. To thwart these attacks and avoid the exploitation of flaws, several approaches have been explored, such as the identification of vulnerabilities or the verification of models using a formal verification. However, these approaches have failed in solving the blockchain crime problem. This article proposes a new anomaly detection approach based on the behavior of smart contracts in the Ethereum blockchain. This formalism will be based on mathematical principles such as state mutation and an approach to verify the behavior of smart contracts.
Smart contracts are a widely researched topic due to their crucial role in blockchain technology. Ethereum, one of the most popular blockchain platforms, utilizes Solidity code for creating secure and transparent transactions through smart contracts. Etherscan provides an extensive collection of Solidity code for Ethereum smart contracts. However, smart contract vulnerabilities can lead to significant financial losses for contract holders. To address this issue, a comprehensive dataset containing numerous defects was collected from etherscan and smartbug-wild. This paper is Proposed a Mathematical model to effectively detect and analyze all possible vulnerabilities with high accuracy, providing an efficient solution for improving smart contract security and ensuring the integrity and reliability of blockchain transactions.