The current large blockchain systems (BTC Lightning network, Ethereum, etc.) are generally facing the problems of low persistence rates and high storage costs. Therefore, users tend to store single modal (textual) information on the existing blockchain systems. Inspired by semantic communication algorithms, this paper presents a new algorithm to solve the serious imbalance between textual and visual modals on blockchains. After semantic sampling of the original visual image, the resulting semantic text will be stored on the chain, and the end users can reconstruct a semantically similar image using the \textbf{R}elative \textbf{O}ptimal \textbf{S}emantic \textbf{I}sotope \textbf{S}election algorithm. Experiments on the DIV2K dataset show that the blockchain with our algorithm can achieve 430,000 times the storage capacity and 550,000 times the persistence rate for the original visual data with acceptable semantic information loss.
As a trusted decentralized application, smart contracts manage a large number of digital assets on the blockchain. Vulnerability detection of smart contracts is an important part of ensuring the security of digital assets. At present, many researchers extract features of smart contract source code for vulnerability detection based on deep learning methods. However, the current research mainly focuses on the single representation form of the source code, which cannot fully obtain the rich semantic and structural information contained in the source code, so it is not conducive to the detection of various and complex smart contract vulnerabilities. Aiming at this problem, this paper proposes a vulnerability detection model based on the fusion of syntax and semantic features. The syntactic and semantic representation of the source code is obtained from the abstract syntax tree and control flow graph of the smart contract through TextCNN and Graph Neural Network. The syntactic and semantic features are fused, and the fused features are used to detect vulnerabilities. Experiments show that the detection accuracy and recall rate of this model have been improved on the detection tasks of five types of vulnerabilities, with an average precision of 96% and a recall rate of 90%, which can effectively identify smart contract vulnerabilities.
Smart contracts are one of the three major characteristics of blockchain, and they are also areas where blockchain has application value and flexibility. In essence, a smart contract is a piece of code implemented in a specific scripting language, which inevitably has the risk of security vulnerabilities. How to accurately and timely detect the vulnerabilities of various smart contracts has become the focus and hot spot of blockchain security research. To detect vulnerabilities in smart contracts, researchers have proposed various analysis methods, including symbolic execution, formal verification and fuzzing. With the rapid development of artificial intelligence technology, more and more deep learning-based methods have been proposed and have achieved good results in several research areas. At present, deep learning-based smart contract vulnerability detection methods have not been investigated and analyzed in detail. This paper first briefly introduces the concept of smart contracts and security events related to smart contract vulnerabilities, then introduces the commonly used smart contract features in deep learning-based methods, and describes the deep learning models commonly used in smart contract vulnerability detection. In addition, in order to further promote the research of deep learning-based smart contract vulnerability detection methods, the recent deep learning-based smart contract vulnerability detection methods are summarized and classified according to their feature extraction forms, and are analyzed and introduced from three perspectives: text processing, static analysis and image processing. Finally, the challenges and future research directions in this field are summarized.
Public blockchains are the digital infrastructure that powers the multi-trillion-dollar economy in cryptocurrencies. Understanding the security and performance of deployed blockchain networks is critically important, especially when the open-membership nature of blockchain results in a large attack surface. However, measuring operational blockchain networks raises ethical concerns and could interfere with the businesses running atop the blockchains. This work presents a survey of the recent measurement studies on the Ethereum networks and discusses their ethical issues, practices, and solutions. The paper also identifies several open ethical challenges faced by blockchain researchers.
Bitcoin Request for Comment 20 (BRC-20) token mania was a key storyline in the middle of 2023. Setting it apart from conventional Ethereum request for comments (ERC)-20 token standards on Ethereum, BRC-20 introduces nonfungibility to Bitcoin through an editable field in each satoshi (0.00000001 Bitcoin, the smallest unit), making them unique. In this article, we pioneer the exploration of this concept, covering its intricate mechanisms, features, and state-of-the-art applications. By analyzing the multidimensional data spanning over months with factual investigations, we conservatively comment that while BRC-20 expands Bitcoin’s functionality and applicability, it may still not match Ethereum’s abundance of decentralized applications and similar ecosystems.
Yijing Lin, Hongyang Du, Dusit Niyato, Jiangtian Nie · 7 authors
The construction of virtual transportation networks requires massive data to be transmitted from edge devices to Virtual Service Providers (VSP) to facilitate circulations between the physical and virtual domains in Metaverse. Leveraging semantic communication for reducing information redundancy, VSPs can receive semantic data from edge devices to provide varied services through advanced techniques, e.g., AI-Generated Content (AIGC), for users to explore digital worlds. But the use of semantic communication raises a security issue because attackers could send malicious semantic data with similar semantic information but different desired content to break Metaverse services and cause wrong output of AIGC. Therefore, in this paper, we first propose a blockchain-aided semantic communication framework for AIGC services in virtual transportation networks to facilitate interactions of the physical and virtual domains among VSPs and edge devices. We illustrate a training-based targeted semantic attack scheme to generate adversarial semantic data by various loss functions. We also design a semantic defense scheme that uses the blockchain and zero-knowledge proofs to tell the difference between the semantic similarities of adversarial and authentic semantic data and to check the authenticity of semantic data transformations. Simulation results show that the proposed defense method can reduce the semantic similarity of the adversarial semantic data and the authentic ones by up to 30% compared with the attack scheme.
Open access
3 source records
Adversarial Robustness in Machine Learning
Big Data and Digital Economy
Physical Unclonable Functions (PUFs) and Hardware Security
Cryptocurrency is a digital payment system, created by non-banks, not subject to any official license, and is mainly mined using blockchain technology. The process of emission of cryptocurrency is called mining, and since cryptography is used, the meaning of "crypto" is mentioned at the beginning of the word currency. Cryptocurrency transactions do not require personal information. This means that users remain largely anonymous and transactions are largely confidential. However, every transaction is recorded, senders and receivers are known to the public. This way, all transactions are tracked, no transaction can be changed, manipulated or deleted afterwards. In this respect, cryptocurrencies are a bit more transparent than digital currencies. Cryptocurrencies and digital wallets, which are the most popular nowadays, are similar but have different functions. In this regard, the article we present is suitable for economically interested individuals or organizations.
With the development of blockchain technology, more and more attention has been paid to the intersection of blockchain and education, and various educational evaluation systems and E-learning systems are developed based on blockchain technology. Among them, Ethereum smart contract is favored by developers for its “event-triggered” mechanism for building education intelligent trading systems and intelligent learning platforms. However, due to the immutability of blockchain, published smart contracts cannot be modified, so problematic contracts cannot be fixed by modifying the code in the educational blockchain. In recent years, security incidents due to smart contract vulnerabilities have caused huge property losses, so the detection of smart contract vulnerabilities in educational blockchain has become a great challenge. To solve this problem, this paper proposes a graph neural network (GNN) based vulnerability detection for smart contracts in educational blockchains. Firstly, the bytecodes are decompiled to get the opcode. Secondly, the basic blocks are divided, and the edges between the basic blocks according to the opcode execution logic are added. Then, the control flow graphs (CFG) are built. Finally, we designed a GNN-based model for vulnerability detection. The experimental results show that the proposed method is effective for the vulnerability detection of smart contracts. Compared with the traditional approaches, it can get good results with fewer layers of the GCN model, which shows that the contract bytecode and GCN model are efficient in vulnerability detection.
In recent years, smart contract vulnerability detection methods mostly view smart contract source code as natural language for processing, which cannot fully capture the semantic and structural features of the source code and has a high rate of false positives and missing positives. To improve the accuracy of vulnerability detection, this paper uses Graph Neural Network to obtain the semantic and structural information of the source code and Convolutional Neural Network to assist learning. We propose a graph neural network-based vulnerability detection model for smart contracts, which transforms smart contracts into control flow graphs, learns graph embedding using graph neural networks, and introduces Convolutional Neural Networks to learn the node order information of control flow graphs, and finally performs vulnerability detection using graph embedding and node order information. Experimenting on real datasets, our accuracy and F1 values are improved and the model can effectively detect smart contract vulnerabilities.
Qing Zhang, Jingyi Du, Peiyu Zheng, Lu Zhang · 8 authors
As one of the most concerned Internet technologies in recent years, blockchain technology is in the process of accelerating its evolution and maturity. The blockchain is gradually integrated with other Internet technologies and applied in many industries, providing decentralized solutions for various industries, realizing innovative storage models, and building a new trust system. As one of the largest international standardization organizations, the Telecommunication Standardization Sector of the International Telecommunication Union (ITU-T) has published standards with great influence in the world, and there are many standards published and under development in the field of blockchain technology, and the types of standards are abundant. This paper first introduces the organizational structure and workflow of ITU. Then it introduces study and achievements on blockchain, including the focus group established by ITU-T and the standards published and under development by multiple study groups. It makes a statistical analysis of the standards, and expects that the blockchain standards will be more diverse in the future to meet the needs of industrial development.
La Blockchain en tant que technologie stocke les traces de ses utilisateurs connectés à Internet lors de leurs échanges de données, de plus, ces traces sont consultables par tous, mais infalsifiables. Elle fonctionne sur l’algorithme de consensus qui garantit la fiabilité de ses canaux de transmission de données sans organe central de contrôle. Face à ces privilèges que possède cette technologie Blockchain, nous l'intégrons dans le cadre de l'Internet of Things (IoT) sous économie d'énergie pour répondre à la problématique suivante : comment décentraliser l'architecture centralisée des appareils IoT économes en énergie et ressources limitées tout en fiabilisant leurs liens ou canaux de communication sur Internet ? Cependant, comme il existe plusieurs blockchains d'algorithmes de consensus différents dans la consommation d'énergie et les ressources utilisées pour fonctionner, nous les analysons dans le cadre de l'Internet of Things pour choisir un algorithme de consensus de blockchain le plus adapté aux appareils IoT économes en énergie et ressources limitées. Après notre analyse, nous avons constaté que l'algorithme de consensus Pure Proof of Stake (PPoS) est simplifié en termes de ressources dans son processus d’adaptation avec un autre réseau et consomme peu d'énergie. C'est ainsi que nous utilisons sa blockchain (Algorand) pour l'intégrer dans le cadre de l'Internet of Things (IoT) en répondant à la problématique posée. Pour le démontrer, nous proposons une architecture d'intégration de la Blockchain et IoT utilisant l'algorithme de consensus (PPoS), une ampoule LED, l’appareil IoT (ESP32) limité en espace de stockage et économe en énergie. Nous réalisons cette architecture décentralisée en connectant entre eux, l’appareil IoT (ESP32), l'ampoule LED, les utilisateurs ou nœuds de la blockchain Algorand d’algorithme de consensus PPoS et Internet. Cela crée donc une interaction pour que notre ampoule LED connectée à Internet grâce à l’appareil IoT (ESP32), soit contrôlable sur Internet via un smartphone ou un ordinateur connecté à notre réseau Blockchain et IoT. Cette interaction est fiable. Car nous pouvons consulter la Blockchain contenant les traces des données échangées entre nos utilisateurs par Etherscan d'Algorand, afin de s’assurer de l'acheminement des données ou informations vers un destinataire de notre réseau décentralisé.
Since its conceptualization, blockchain technology has witnessed continuous and rapid development, bringing profound changes to computer science, law, and economics. In 2008, the initial blockchain system was merely a growing list of records linked together using cryptography. Today, however, blockchains have become the foundation of most digital currencies, robust cloud computing platforms, and dependable databases for tracking supply chain information. Despite challenges and controversies, blockchain technology has the potential to help build a trustworthy and efficient digital world.
People are getting familiar with cryptocurrencies because of the rapid development of cryptography, and bitcoin, a traditional decentralized digital currency, becomes famous. Thus, it is necessary to establish a digital currency allocation framework. Two existing methods both share the same goal of reaching blockchain consensus; however, the processes are different: The proof of Work system is completely related to tasks, but the Proof of Stake system is related to tokens. Hence, service providers are more than glad to apply the Proof of Work theory after distinguishing the difference between these two systems; this system which does not have high limitations is more fair and balanced. To enhance the traditional Proof of Work system, Artificial Intelligence can properly help and make the new framework works more efficiently. AI model can pre-assign a trustworthy score via the IP address, and then it can take the responsibility to generate the puzzle for the qualification. After the model verifies the output, the trustworthy score can increase or decrease based on the performance. Finally, it can establish a loop from the trustworthy score to puzzle difficulty, and then back to the trustworthy score. Therefore, an AI assistant can accurately monitor the entire transaction process and ensure validation to be environmentally friendly.
Lin Chen, Qingchun Yu, Wei Liang, Jiahong Cai · 6 authors
With the increasing growth of electronic medical data, the difficulties of data sharing among medical institutions and the leakage of data privacy have become the focus of the public and medical workers. The blockchain has the characteristics of decentralization, traceability, and immutability, which can provide new ideas for fine-grained secure access to medical research. This article first introduces blockchain and blockchain-based privacy protection technology; then analyzes the advantages and disadvantages of electronic medical records, and introduces the current development status of electronic medical records based on blockchain technology; then from data encryption, access the three aspects of control and transaction anonymity introduce the medical data privacy protection method based on blockchain technology; finally, the full text is summarized and prospected.
Since its inception, blockchain technology attracts great attention from the industry and academia. With its development, cryptocurrencies such as bitcoin based on blockchain technology gradually emerge and enter the financial field. Meanwhile, malicious behaviors aimed at bitcoin become more and more common and cause huge damage to cryptocurrency users and the evolution of blockchain technology, which prompt researchers to establish various models to deal with this problem. In this paper, we collected the historical bitcoin transaction dataset and extracted features from it. After standardizing features, we used an unsupervised learning model based on Generative Adversarial Networks (GAN) to detect dataset containing more than 30 million normal and 108 malicious samples and reached a precision of 23% and recall value close to 100%.
Blockchain Technology Applications and Security
Big Data and Digital Economy
Physical Unclonable Functions (PUFs) and Hardware Security
As the global industrial complex gears toward fulfilling the tenets of Industry 4.0 and beyond, technologies such as distributed ledger technologies, digital twins, and artificial intelligence become pivotal enablers. In the last decade, metaverse as a concept and technology found its place among crucial enablers for technology and digital advancement across several engineering domains. Metaverse has the potential to combine the elements from distributed computing platforms, the digital evolution of physical systems, and advanced learning systems to unearth a fully digitized world of comparative properties of the real world. We should ensure the privacy, integrity, and confidentiality of personal data. These requirements will lead to proper identity management in the metaverse. Given the complex nature of the metaverse, traditional centralized systems may not offer a viable identity management solution. Therefore, this study explores a decentralized identity management system called the Self-sovereign Identity (SSI) as a logical alternative to traditional centralized identity management systems. The proposed holistic framework aims to ignite new ideas and discussions related to the combined deployment of DLT, SSI, and metaverse to inspire new implementation areas within the Industry 4.0 environment. The paper also discusses various opportunities, enablers, technical \& privacy aspects, legislation requirements, and other barriers related to SSI implementation.