Blockchain Papers

Zhiyue Yan, Xi Zhao, Yang Liu, Xin Luo

No abstract is available for this record.

Aryan Patel, Kartikeya Chauhan, Saarthak Maini, Mukta Goyal

This paper into the critical issue of vulnerability detection in smart contracts, focusing on identifying vulnerabilities, proposing mitigation strategies, and developing techniques for detecting Ponzi schemes within smart contracts. By understanding and addressing these vulnerabilities, we aim to enhance the security and robustness of blockchain-based applications and ecosystems.

Algimantas Venčkauskas, Vacius Jusas, Dominykas Barisas, Boriss Mišņevs

Sharing cyber threat intelligence (CTI) can significantly improve the security of information technology (IT) in organizations. However, stakeholders and practitioners are not keen on sharing CTI data due to the risk of exposing their private data and possibly losing value as an organization on the market. We present a model for CTI data sharing that maintains trust and confidentiality and incentivizes the sharing process. The novelty of the proposed model is that it combines two incentive mechanisms: money and reputation. The reputation incentive is important for ensuring trust in the shared CTI data. The monetary incentive is important for motivating the sharing and consumption of CTI data. The incentives are based on a subscription fee and a reward score for activities performed by a user. User activities are considered in the following three fields: producing CTI data, consuming CTI data, and reviewing CTI data. Each instance of user activity is rewarded with a score, and this score generates some value for reputation. An algorithm is proposed for assigning reward scores and for recording the accumulated reputation of the user. This model is implemented on the Hyperledger Fabric blockchain and the Interplanetary File System for storing data off-chain. The implemented prototype demonstrates the feasibility of the proposed model. The provided simulation shows that the selected values and the proposed algorithm used to calculate the reward scores are in accordance with economic laws.

Garima Kohli, Saloni Devi

The emphasis on cryptocurrencies in the investment and funding sectors is evolving. This study investigates the intersection of cryptocurrency and cybersecurity. In addition to this, the study also explores the opportunities and challenges of cryptocurrency. The research is based on an extensive review of the literature to obtain insights into the challenges and opportunities of leveraging cryptocurrencies and cybersecurity. The existing review is valuable for academicians, managers, and scholars as well as for those looking to have an understanding of these budding financial instruments.

H.K. Verma

The coming of cryptocurrencies has, amazingly, changed the face of the financial space. It has opened up opportunities and challenges in the field of money laundering. The deep impact of cryptocurrencies on the practice of money laundering becomes a detailed study. Since digital currencies are embedded with inherent characteristics, such as anonymity, decentralization, and the ease of performing cross-border transfers, criminals have now found new ways to conceal their illicit financial activities. The paper critically reviews how cryptocurrencies are used in money laundering schemes, evaluates the effectiveness of current legal provisions and anti-money laundering measures, and reviews case studies that exemplify real-world applications and challenges to regulatory bodies. Moreover, it offers recommendations on the use of new technologies, like blockchain analytics, toward better detection and prevention of money laundering through cryptocurrency. The paper thus provides a range of useful insights, associated with recommendations for the strengthening of the global regulatory framework in dealing with the increased threat of cryptocurrency money laundering, through a synthesis of the literature review, case analysis, and expert interviews. The paper contributes to this debate by providing insight into the challenges that regulatory authorities face and making recommendations to improve anti-money laundering efforts in the cryptocurrency space. This is done through an in-depth review of recent cases and legislation in this area. The findings were that, though cryptocurrencies pose a great challenge, innovative technology solutions coupled with international cooperation can play a vital role in mitigating the risks associated with cryptocurrency-based money laundering.

Bal Ram, Pratima Verma

Blockchain technology has revolutionised how data is stored, managed, and secured. Its decentralised, transparent, and immutable nature presents unique advantages for data security. This paper delves into the application of blockchain technology in enhancing data security, exploring its fundamental principles, mechanisms, real-world applications, benefits, and challenges. By examining case studies across various industries, this paper aims to demonstrate the transformative potential of blockchain technology in securing data and protecting against cyber threats.

Ardhian Dwiyoenanto, Adi Sulistiyono, Hartiwiningsih Hartiwiningsih

The advance of Information Technology is closely related to and has a direct impact on the development of people’s lives. One of the real technological advancements that plays a major role in creating evolution in the community life order is Internet progress. As time passes, the internet world continues to experience rapid development, such as Metaverse, Non-Fungible Tokens (NFTs), and Cryptocurrency. Meanwhile, the change of regulations and legal products that are not as fast as the advance of the internet and the business world raises their abuse potential as means of Money Laundering Crime. The research method used was normative juridical with analytical descriptive research specifications. Metaverse, NFTs, and Cryptocurrency are relatively new phenomena in this globalization era. The lack of regulation and the high volatility of price characteristics that are strongly influenced by public interest make them potential as means to hide or disguise the origin of assets from criminal acts. So, this research was conducted to analyse the potential use of Metaverse and Non-Fungible Tokens as means of money laundering.

Rakibul Hasan Chowdhury

The integration of Blockchain technology and Artificial Intelligence (AI) is revolutionizing data management and business intelligence. Blockchain, with its decentralized, immutable ledger, ensures data integrity and security, while AI enhances data analysis through advanced algorithms and predictive capabilities. This article explores the synergy between these two transformative technologies, examining how their combined strengths can address modern challenges in data security and business operations. The paper begins with an overview of Blockchain and AI, detailing their foundational principles and recent advancements. It then delves into their applications in enhancing data security, highlighting Blockchain's role in providing encryption and immutability and AI's capabilities in threat detection and response. The discussion extends to their impact on business intelligence, showcasing how Blockchain contributes to transparent and verifiable data, while AI drives advanced analytics and decision-making. Real-world case studies illustrate successful implementations of Blockchain and AI integration, demonstrating their potential to revolutionize various industries. The article also addresses technical challenges, privacy concerns, and regulatory issues associated with these technologies. Finally, it outlines future directions for research and innovation, emphasizing the need for continued exploration of their combined potential. By providing a comprehensive analysis of Blockchain and AI's transformative impact, this article aims to offer valuable insights for researchers, practitioners, and policymakers seeking to leverage these technologies for improved data security and business intelligence.

Jiblal Upadhya, Kritagya Upadhyay, Arpan Man Sainju, Samir Poudel · 7 authors

In recent years, Smart Contracts have gained in popularity, facilitating billions of US Dollars in daily transactions. However, the recent increase in smart contract vulnerabilities threatens to undermine trust in the technology. The study aims to detect and address potential vulnerabilities in smart contracts in blockchain technology through a comprehensive analysis of four principal modalities: Solidity source code, bytecode, opcode, and intermediate representations. This proactive identification of vulnerabilities can contribute to bolstering the security and dependability of blockchain-based systems. In this paper, we propose a novel multimodal Transformer architecture named QuadraCode AI, utilizing these four distinct modalities. Unlike traditional unimodal analysis, multimodal analysis can provide a more holistic understanding of both the semantic and syntactical contexts of smart contracts to identify underlying vulnerabilities. By employing advanced data fusion techniques such as cross-attention and concatenations across 12 different multimodal frameworks, our approach enhances the detection capabilities beyond traditional unimodal approach. Notably, the framework that integrates opcode with bytecode achieves an impressive average F score of 86%, demonstrating the effectiveness of our method.

М. М. Виноградова

The article examines a new object of forensic economic examination – cryptocurrency. The author provides classic definitions of an object of forensic examination and establish the differences of cryptocurrency from the traditionally understood objects of both forensic examination in general and economic examination in particular. The main difference of cryptocurrency from other currencies and objects of investigation is its virtual nature, lack of affiliation with the material world. Two main points of view on the essence of cryptocurrency are analyzed: as a basis and tool for the development of new effective forms of payments, exchange of goods, and as an object and instrument of criminal activity. A definition of cryptocurrency is given as interpreted by the FATF – Financial Action Task Force. It is identified which issues related to the circulation of cryptocurrency can be attributed to the competence of a forensic expert-economist, and which – to the field of computer forensics. The author also describes the features of cryptocurrency that must be taken into account when considering it as an object of forensic economic examination.

Mahendran Varma -, Batani Raghavendra Rao -

The main purpose of this study is to provide a comprehensive assessment of the importance of cryptocurrencies in terms of money laundering risk and to provide detailed information on money laundering techniques and structures. An analysis of how cryptocurrencies impact local and international money laundering is also being explored to clarify the facts. The article attempts to provide a better understanding of this emerging problem by providing information on the use of digital currency to change the money laundering landscape. To clarify the review, the review is divided into two main parts: The first part will focus on the theoretical framework of the money laundering process, the process complexity of cryptocurrencies and the ecosystems surrounding them. The second part will examine whether virtual currencies are suitable for money laundering, the various features that make virtual currencies ideal for such activities, and the creation of new emerging technologies will also be discussed. This document is designed to provide policymakers, regulators, and law enforcement with useful information and strategic solutions to address the challenges of cryptocurrency money laundering through many of these methods.

Andrei Arusoaie, Ştefan-Claudiu Susan

No abstract is available for this record.

Ningbo Zhu, Jinghan Sun, Shuyi Zhang, Xinyao Sun · 5 authors

In the rapidly evolving landscape of blockchain technology, security emerges as a paramount concern. This paper introduces an innovative blockchain security threat awareness platform, designed to comprehensively address the multifaceted security challenges within blockchain networks, particularly focusing on Ethereum contracts. Central to the platform is a dual-database architecture, blending a NoSQL database with a graph database, enhancing data management, and enabling intricate transaction network visualizations. The platform's Threat Detection module, utilizing Large Language Models (LLMs) in conjunction with traditional methods, offers a novel approach to identifying and categorizing vulnerabilities in Ethereum smart contracts. Complementing this, the Threat Evidence Collection module provides detailed post-attack analysis, tracing transactions to their sources and evaluating address risks. This module's capabilities extend to producing statistical reports, including the transactional history and risk evaluation of individual addresses. Demonstrated on the Ethereum blockchain, the platform showcases its proficiency in handling complex data, rapid threat detection, and extensive forensic analysis, presenting a robust solution to fortifying blockchain security and offering a proactive defense mechanism for users and developers in the blockchain environment.

Benlemlih Youssef, Berrada Ismail

This paper examines the actions taken by commercial banks to develop effective surveillance strategies for the identification of the origins of clients' digital assets within the cryptocurrency sector. Informed by the European Union Agency for Cybersecurity (ENISA) guidelines, this study integrates insights from compliance expert interviews, a case study of Barclays, and a thorough analysis of existing literature to enhance cybersecurity measures in response to increasing threats in digital transactions such as cryptojacking. The research emphasizes constructing a methodical approach for organizations to enhance their established defenses against cryptocurrency-related risks by integrating ENISA principles. The proposed strategy focuses on improving transaction traceability and monitoring capabilities of blockchain technologies while acknowledging technological challenges related to regulatory and safety compliance. The provided in-depth analysis of blockchain and cryptocurrency technologies present in this paper advocates for strict regulatory frameworks to navigate these complexities and enhance overall resilience towards this threat.

Stefan Scharnowski

Cryptocurrencies, especially privacy coins, conceal the flow of money. Similarly, the dark web obscures the flow of internet traffic, increasing anonymity. In this paper, I provide evidence that secondary market trading activity in privacy coins is linked to dark web traffic, although their pricing remains mostly unaffected. This finding holds after considering various controls and comparing similar privacy and non-privacy coins. However, when disentangling dark web traffic by country of origin, I find that privacy coin prices correlate positively with traffic from China, while trading volume is mainly driven by users from Russia and Iran.

Suraj Shamsundar Jain, Huancheng Zhou, Guofei Gu

Distributed Applications (DApps), powered by smart contracts, have sparked a significant transformation in the Web3 ecosystem by enabling the execution of real-world contracts on decentralized networks. However, the growing popularity of DApps has also led to an increase in malicious activities exploiting smart contracts, thereby exposing users to greater financial risks. Inspired by the FICO score system in traditional finance, we introduce WIRE, a reputation engine designed to evaluate the trustworthiness of deployed DApps. WIRE first derives diverse properties from contract activities, rather than relying solely on potentially irrelevant or unavailable source code. Based on selected properties, WIRE trains a machine learning model for assessing the trustworthiness of individual contracts. Further-more, WIRE utilizes a bytecode disassembler to identify related contracts of a DApp, thus determining its overall trustworthiness score. Moreover, WIRE's dashboard offers explainable and detailed reports that are accessible to users without professional knowledge. The evaluation results show that WIRE can provide a reliable and explainable reputation score for DApps. As a result, WIRE's users can distinguish between benign and malicious DApps or contracts with a high confidence.

Hanting Chu, Pengcheng Zhang, Hai Dong, Yan Xiao · 5 authors

Abstract The growing popularity of smart contracts in various areas, such as digital payments and the Internet of Things, has led to an increase in smart contract security challenges. Researchers have responded by developing vulnerability detection tools. However, the effectiveness of these tools is limited due to the lack of authentic smart contract vulnerability datasets to comprehensively assess their capacity for diverse vulnerabilities. This paper proposes a D eep L earning‐based S mart contract vulnerability G eneration approach (SGDL) to overcome this challenge. SGDL utilizes static analysis techniques to extract both syntactic and semantic information from the contracts. It then uses a classification technique to match injected vulnerabilities with contracts. A generative adversarial network is employed to generate smart contract vulnerability fragments, creating a diverse and authentic pool of fragments. The vulnerability fragments are then injected into the smart contracts using an abstract syntax tree to ensure their syntactic correctness. Our experimental results demonstrate that our method is more effective than existing vulnerability injection methods in evaluating the contract vulnerability detection capacity of existing detection tools. Overall, SGDL provides a comprehensive and innovative solution to address the critical issue of authentic and diverse smart contract vulnerability datasets.

Fernando Richter Vidal, Naghmeh Ivaki, Nuno Laranjeiro

The number of applications supported by blockchain smart contracts has been greatly increasing in recent years, with smart contracts now being used across several domains, such as the music industry, finance, and retail, to name a few. Despite being used in business-critical contexts, the number of security vulnerabilities in smart contracts has also been increasing, with many of them being exploited and resulting in huge financial and reputation losses. This is despite the enormous effort that is being placed into the research and development of vulnerability detection tools and techniques, which have also greatly increased in number and type in the last few years. Motivated by the recent increase in both vulnerabilities and vulnerability detection techniques, this paper reviews the latest research in smart contract vulnerability detection, emphasizing the techniques being used, the vulnerabilities targeted, and the characteristics of the dataset used for evaluating the technique. We mapped the vulnerabilities against two common vulnerability classification schemes (DASP and SWC) and performed a consolidated analysis. We identified the current research trends and gaps in each technique and highlighted future research opportunities in the field. • A categorization of smart contract vulnerability detection techniques. • The identification of smart contract vulnerabilities that are the target of current vulnerability detection tools. • An analysis of the datasets used in smart contract vulnerability research.

Jiachi Chen, Chong Chen, Jiang Hu, John Grundy · 7 authors

Smart contract developers frequently seek solutions to developmental challenges on Q&A platforms such as Stack Overflow (SO). Although community responses often provide viable solutions, the embedded code snippets can also contain hidden vulnerabilities. Integrating such code directly into smart contracts may make them susceptible to malicious attacks. We conducted an online survey and received 74 responses from smart contract developers. The results of this survey indicate that the majority (86.4%) of participants do not sufficiently consider security when reusing SO code snippets. Despite the existence of various tools designed to detect vulnerabilities in smart contracts, these tools are typically developed for analyzing fully-completed smart contracts and thus are ineffective for analyzing typical code snippets as found on SO. We introduce SOChecker, the first tool designed to identify potential vulnerabilities in incomplete SO smart contract code snippets. SOChecker first leverages a fine-tuned Llama2 model for code completion, followed by the application of symbolic execution methods for vulnerability detection. Our experimental results, derived from a dataset comprising 897 code snippets collected from smart contract-related SO posts, demonstrate that SOChecker achieves an F1 score of 68.2%, greatly surpassing GPT-3.5 and GPT-4 (20.9% and 33.2% F1 Scores respectively). Our findings underscore the need to improve the security of code snippets from Q&A websites.

Azreen Shafieqah Asri, Faizatul Fitri Boestamam, Haslizaidi Zakaria, Mohammad Amir Alam Rahim Omar · 6 authors

With the rapid expansion of the Industrial Internet of Things (IIoT), integrating devices, machines, and systems to optimize operations and enable data-driven decision-making, ensuring robust security measures is essential. While blockchain has shown the potential to upgrade traditional authentication methods in IIoT environments, vulnerabilities persist. This paper introduces two innovative methods to enhance blockchain-based authentication in IIoT: first, integrating AI-driven anomaly and threat detection into the blockchain authentication scheme; second, implementing Ethereum smart contracts for enhanced authentication with a two-factor authentication (2FA) system and GFE algorithms. By combining AI for anomaly detection with decentralized smart contracts and blockchain-based 2FA, and leveraging GFE algorithms to enhance blockchain capabilities, the proposed scheme aims to significantly fortify security measures. This integration offers a resilient defense against evolving threats, ensuring transparency, adaptability, and heightened security in IIoT applications.

Chaofan Dai, Huahua Ding, Wubin Ma, Yahui Wu

Smart contracts are decentralized applications de-ployed extensively on blockchain. Due to their economic nature, vulnerabilities in smart contracts can lead to potential significant economic and property losses, disrupting the stable ecosystem of Ethereum. Therefore, the detection of smart contract vul-nerabilities is of paramount importance. Current mainstream methods for smart contract vulnerability detection rely on heuris-tic algorithms based on manual design, which lack reusability across different application scenarios, are time-consuming, and exhibit suboptimal accuracy. To enhance vulnerability detection effectiveness, a method tailored for timestamp vulnerabilities in smart contracts is proposed, named SESCD, based on self-ensembling pretraining. The proposed approach first identifies potential data propagation paths for timestamp vulnerabilities, prunes them, and leverages self-ensembling pretrained models to learn about these propagation paths. Furthermore, the training process is optimized through knowledge distillation to improve the model's ability to detect whether smart contracts contain timestamp vulnerabilities. SESCD demonstrates superior vulner-ability detection and generalization capabilities, alleviating performance instability issues caused by insufficient training data. To validate the effectiveness of SESCD, comparative experiments are conducted on a real-world dataset of smart contracts against 13 mainstream smart contract vulnerability detection methods. Experimental results show that SESCD achieves precision, recall, and F1 scores of 0.91, 0.93, and 0.92 respectively in detecting timestamp vulnerabilities. Compared to the 13 mainstream methods, SESCD exhibits an average relative improvement of 28%, 30%, and 30%, significantly enhancing the detection capabilities of timestamp vulnerabilities.

Derek Liu, Francesco Piccoli, Victor S. Fang

No abstract is available for this record.

Mui D. Nguyen, Tuan M. Nguyen, Thang C. Vu, Tien Minh Ta · 6 authors

The paper evaluates potential applications of blockchain technology in enhancing the security and reliability of Wireless Sensor Networks (WSNs). The existing vulnerabilities in WSNs, such as concerns regarding data integrity and security, demand innovative security solutions. Through systematic analysis, this paper provides valuable insights to expand understanding of WSNs security, explaining the feasibility and benefits of deploying blockchain technology. Possible attacks in the networks are classified to point out either risks or potential solusions to protect the networks. By exploring the integration of Blockchain within WSNs, the paper highlights its potential to minimize various security risks. In addition, this work discusses the challenges and considerations associated with implementing Blockchain in WSNs. Overall, this paper contributes on securing WSNs and underscores the role of blockchain technology as a promising way for enhancing security of WSNs.

Xia Feng, Qichen Shi, Xingye Li, Haiyang Liu · 5 authors

No abstract is available for this record.