Blockchain Papers

Mohammad N. Alanazi

No abstract is available for this record.

Gioia Arnone, Shiwani Arora Thakur

In this paper, we analysis the data for age, country, gender profession in Cryptocurrencies at business and finance sector. Also we considered the IT, finance and Cryptocurrencies for the further investigation. Experts split into three groups of comparably equal sizes when considering the importance of bad reputation as a result of criminal activities (e.g., money laundering, silk road) (27.97%, 21.68% and 31.47%), which was found to be slightly, moderately or greatly important.

Sharad Agarwal, Gilberto Atondo Siu, Marilyne Ordekian, Alice Hutchings · 6 authors

No abstract is available for this record.

Dimitris Kafteranis, Hüseyin Ünözkan, Umut Türkşen

Non-Fungible Tokens (NFTs) is a new virtual asset phenomenon the trade of which has spread quickly without any regulationas no legislation has been adopted in the EU, USA or the UK where the majority of NFT trading takes place. Concerns have been raised about NFTs and their relation to fraud and money launderingasanonymity and price volatility of NFTs create a unique and profitable asset for criminals. This paper addressestwomain issues: (1) trading statistics on NFTs,their analysis,and if and to what extent NFTs are used for financial crimes purposes; and (2) the legal challenges posed by the misuse of NFTs for fraud and other economic crimes. The final section of this paper provides feasible regulatory and business solutions that can help businesses to mitigate risks emanating from NFTs. It is argued that legal scholars, businesses and/or regulators cannot solve the challenges and risks posed by NFTs on their own, requiringmultidisciplinary research from academia and knowledge exchange between private and public stakeholders to close this gap.

Jinggang Li, Gehao Lu, Yulian Gao, Feng Gao

With the proliferation of blockchain technology in decentralized applications like decentralized finance and supply chain and identity management, smart contracts operating on a blockchain frequently encounter security issues such as reentrancy vulnerabilities, timestamp dependency vulnerabilities, tx.origin vulnerabilities, and integer overflow vulnerabilities. These security concerns pose a significant risk of causing substantial losses to user accounts. Consequently, the detection of vulnerabilities in smart contracts has become a prominent area of research. Existing research exhibits limitations, including low detection accuracy in traditional smart contract vulnerability detection approaches and the tendency of deep learning-based solutions to focus on a single type of vulnerability. To address these constraints, this paper introduces a smart contract vulnerability detection method founded on multimodal feature fusion. This method adopts a multimodal perspective to extract three modal features from the lifecycle of smart contracts, leveraging both static and dynamic features comprehensively. Through deep learning models like Graph Convolutional Networks (GCNs) and bidirectional Long Short-Term Memory networks (bi-LSTMs), effective detection of vulnerabilities in smart contracts is achieved. Experimental results demonstrate that the proposed method attains detection accuracies of 85.73% for reentrancy vulnerabilities, 85.41% for timestamp dependency vulnerabilities, 83.58% for tx.origin vulnerabilities, and 90.96% for integer Overflow vulnerabilities. Furthermore, ablation experiments confirm the efficacy of the newly introduced modal features, highlighting the significance of fusing dynamic and static features in enhancing detection accuracy.

Xueli Shen, Mingfeng Li

With the advancement of blockchain technology, smart contracts, as one of its core applications, have been widely utilized across various industry sectors. However, smart contracts face significant security challenges, with reentrancy vulnerability being a typical concern. In order to effectively detect reentrancy vulnerabilities in smart contracts, this paper proposes a parallel hybrid model based on deep learning. The proposed method initially employs Word2Vec model for word embedding, preprocessing, and feature vectorization of the smart contract code data. Subsequently, a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is employed for feature extraction and sequence modeling. Simultaneously, the method utilizes a parallel approach combining Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) with CNN-LSTM to enhance the model's learning capabilities and efficiency in handling sequential data. Finally, a fully connected layer and a Softmax classifier are employed to classify the extracted features. Through a series of experiments and performance evaluations, the proposed method demonstrates significant improvements in key metrics such as precision, recall, and F1 score compared to traditional methods and single deep learning approaches, achieving 91.66%, 90.16%, and 90.90% respectively.

Shunhui Ji, Congxiong Huang, Hanting Chu, Xiao Wang · 6 authors

No abstract is available for this record.

Xingwei Lin, Mingxuan Zhou, Sicong Cao, Jiashui Wang · 5 authors

No abstract is available for this record.

GuoJin Sun, Chi Jiang, Jinqing Shen, Yin Zhang⋆

No abstract is available for this record.

Lihan Zou, Changhao Gong, Zhen Wu, Jie Tan · 7 authors

No abstract is available for this record.

Yuxi Zhang, Haifeng Guo, Biliang Wang, Yunlong Wang · 5 authors

No abstract is available for this record.

Deepali Joshi, Sangam Patil, Sayee Chauhan, Tanuj Baware · 6 authors

The smart agreement is one of the most utilized makes use of blockchain and an important issue of the blockchain ecosystem. The blockchain-based totally credit device suffers from common smart contract safety troubles, which additionally reason for big monetary losses. As an end result, smart contract’s security and dependability are of exquisite interest to researchers from all over the international. First, the use of the three levels of this survey—the Solidity code layer, the EVM execution layer, and the Block dependency layer—not unusual kinds and standard instances of smart contract vulnerabilities are explained. The nation of the artwork on this difficulty is likewise evaluated, and the existing equivalents are divided into five companies: formal verification, symbolic execution, fuzzing detection, intermediate illustration, and deep getting to know.

Fabian Teichmann, Sonia Boticiu

No abstract is available for this record.

Khalid Al‐Khatib, Sayel Abualigah

Money laundering has significantly increased in recent years as a result of the quick growth of financial systems and the emergence of cryptocurrencies. These crimes threaten the stability of both economic and social systems and impact the security of public and private financial institutions. In response, detecting and preventing money laundering has become a top priority for financial systems, which rely on deep analysis of assets and the nature of funds to uncover illegitimate sources. With the rise of cryptocurrencies like Bitcoin, criminals have adopted new technologies and channels to cover up their illegal activities. This research aims to develop a new deep learning architecture for detecting illegitimate Bitcoin transactions in order to prevent Bitcoin laundering crimes. In this study, a deep investigation of the previous works will be conducted for better understanding of the problem statement. Besides, a new framework will be designed to exceed the performance of current best practices with regard to accuracy. Ultimately, the findings of this research will aid the financial sector in combating Bitcoin laundering activities and strengthening the security of digital financial transactions. An elliptic dataset used for bitcoin transactions that belong to real entities, where it consists of 203,769 nodes and 234,355 edges for illicit and licit transactions. The licit transaction families are licit services, exchanges, miners, and wallet providers. While the illicit transaction families are malware, frauds, Ponzi (fraud) schemes, ransomware, and terrorist organizations. The proposed model semi-supervised generative adversarial network (SGAN) utilized to complete the labelling process of the unknown entities. Based on the experimental results, the proposed model significantly improved upon previous methods, achieving a 98% success rate in accurately predicting illicit transactions. Thus, we consider it as a competitive stand in terms of anti-laundering for the Bitcoin cryptocurrency.

Yevgeniy A. Ignatenko

The article discusses certain features of the legal regulation of cryptocurrency, taking into account the specifics of building a model for the functioning of blockchain networks, which consists in combining various tools, technologies and principles that form a logical and secure structure for distributed data storage. The problems of criminal law protection of digital currency are highlighted and the most common scientific approaches are identified, on the basis of which the author indicates that the use of the concepts of “digital currency” and “cryptocurrency” as synonyms does not correspond to the legislative definition of digital currency, since the concept of cryptocurrency is wider in content than the concept of digital currency. As a result of a comprehensive analysis of the norms of the Federal Law of July 31, 2020 No. 259-FZ On Digital Financial Assets, Digital Currency and Amendments to Certain Legislative Acts of the Russian Federation, the author comes to the conclusion that digital currency is exclusively a “domestic” currency. Based on the study of judicial practice, it is shown that the broadest possible interpretation of the concept of “other property”, which includes digital currency and cryptocurrency, is allowed. It indicates the presence of judicial acts in criminal cases, in which the subject of a crime are such cryptocurrencies as, for example, bitcoin. Attention is focused on the importance of civil law regulation of cryptocurrencies for their criminal law protection and the range of social relations that develop regarding cryptocurrencies subject to protection by means of criminal law is determined.

Saulius Masteika, Eimantas Rebždys, Kęstutis Driaunys, Alfreda Šapkauskienė · 6 authors

No abstract is available for this record.

Sawanya Rattanabunno, Warodom Werapun

This paper introduces an approach to detect a thief’s wallet by analyzing linked transactions connected to centralized exchanges mandating KYC verification. The proposed methods are designed to provide valuable insights into the intricate flow of funds and the behavioral patterns of malicious actors, explicitly focusing on thieves. By inputting a thief’s wallet address, our analysis examines associated transactions, capturing vital details such as the amount of Ethereum (ETH) transferred and all related transactions, including those involving ERC-20 tokens. The primary objective is comprehending the money flow among different addresses, emphasizing wallets interacting with centralized exchanges. By monitoring the movements of ETH and ERC-20 tokens, this analysis aims to identify suspicious transactions and uncover behavioral patterns indicative of illicit activities. Leveraging this analytical approach, we can track the thief’s identity, making a substantial contribution to the prevention and detection of cryptocurrency theft, thus ensuring the security of blockchain ecosystems.

Lina Li, Yang Liu, Guodong Sun, Nianfeng Li

Smart contract is the core of blockchain operation, and contract vulnerability will cause huge economic losses. Therefore, effective smart contract vulnerability detection is of vital importance and attracts more and more attention. In this paper, we propose a vulnerability detection model (VDM-AEI) based on automatic feature extraction and feature interaction. For the first time, this model converts smart contracts into gray images and uses VGG16 and GRU models to automatically extract vulnerability features and filter effective features, respectively. Then, a contract graph and an expert knowledge feature vector are constructed by using commonly used methods as part of feature construction. Next, AutoInt and DCN networks are used to build a dual feature interaction network to obtain more abundant vulnerability feature information, which extracts high-dimensional nonlinear features from the low and sparse features of the contract graph feature vector and the expert knowledge-defined feature vector. Finally, all ouput features of GRU, AutoInt and DCN networks are integrated to obtain vulnerability classification results through fully connected neural networks. We conducted extensive experiments on the ESC and VSC datasets for reentrancy vulnerabilities, timestamp dependency vulnerabilities, and infinite loop vulnerabilities. The experimental results prove the effectiveness and accuracy of the VDM-AEI model. Compared with the latest vulnerability detection model CGE, the accuracy rates of the 3 types of vulnerability detection are improved by 10.85%, 6.18%, and 12.34%, respectively. In addition, the predicted F1 scores of VDM-AEI are all greater than 95%, and the recall rate is no less than 94%.

Bowen He, Yuan Chen, Zhuo Chen, Xiaohui Hu · 9 authors

The prosperity of Ethereum attracts many users to send transactions and trade crypto assets. However, this has also given rise to a new form of transaction-based phishing scam, named TxPhish. Specifically, tempted by high profits, users are tricked into visiting fake websites and signing transactions that enable scammers to steal their crypto assets. The past year has witnessed 11 large-scale TxPhish incidents causing a total loss of more than 70 million.

Brandon Dulisse, Nathan T. Connealy, Matthew W. Logan

No abstract is available for this record.

Andrew Childs

Cryptomarkets are increasingly requiring users to purchase products with Monero (a ‘privacy coin’) to further obfuscate the digital trail of money compared to conventional cryptocurrencies (e.g. Bitcoin). This is the first study to explore how cryptomarket communities are used to facilitate norms and behaviours to expedite these emerging cryptocurrency practices. Through a qualitative analysis of Monero threads in a Reddit cryptomarket community (3451 total posts), this research illustrates how online communities often underpin the adoption of new technologies in cryptomarkets. The findings reveal: how the online community functions, adapts, and fails to support cryptocurrency transitions; the appraisal and contestation of cryptocurrency risks; and the ideological drivers and symbolic resources used to align community practices to adopt Monero. This research contributes to an understanding of the processes that underpin the constant evolution of online illicit markets as human and non-human elements are constantly re-assembled.

J J Lohith, Kunwar Singh, Bharatesh Chakravarthi

No abstract is available for this record.

S. Porkodi, D. Kesavaraja

No abstract is available for this record.

Rahmeh Ibrahim, Qasem Abu Al‐Haija

Blockchain technology provides a data structure with intrinsic security qualities, such as cryptography, decentralization, and consensus, that guarantee the integrity of transactions. It has wide-ranging applications, including the Internet of Things (IoT), health, intelligent manufacturing, finance, and many more. In this chapter, we shed light on the blockchain solutions for cyber criminals, concepts, elements, structure, and other aspects of blockchain utilization. Specifically, this chapter extends the elaboration on the blockchain, the blockchain components, the blockchain architecture, features, types, and limitations. Also, this chapter will extend the elaboration on cyber criminals, their types, their security needs, blockchain solutions, issues, challenges, and difficulties of using blockchain technology to fight cyber crimes. This chapter deepens the knowledge of blockchain solutions for cyber criminals and provides more insights to readers about blockchain, cyber attacks, cyber criminals, and their countermeasures.