Cybersecurity has encountered significant challenges, including identity theft, data breaches, and evolving threats to cyberspace. The decentralized, immutable, and transparent characteristics of blockchain technology have significantly enhanced its efficacy in bolstering cybersecurity. The application of blockchain in identity management, data privacy, and threat mitigation is examined, indicating it as a technology that addresses vulnerabilities inherent in conventional systems. Their capacity to enhance security, user autonomy, and trust is evidenced by decentralized Digital Identities (DIDs), smart contract-enforced data utilization policies, and blockchain-based threat intelligence systems. Despite its robustness, blockchain faces challenges, including scalability, interoperability, regulatory compliance, and energy consumption. Emerging trends (blockchain integration with AI and ML, quantum-resistant cryptography, etc.) are moving toward innovative solutions to these issues. Furthermore, the overlap of blockchain with zero-trust architectures highlights the utility of blockchain in present-day cybersecurity frameworks. The use of blockchain in finance is emphasized through this study as a demand for industry collaboration, scalable innovations, and a supportive regulatory framework to unleash the potential of the blockchain. A blockchain solution can help fill existing gaps in security strategies and pave the way to adoptive security.
Detecting vulnerable smart contracts has a direct effect on blockchain security because it helps users avoid using these contracts. In this study, the problem of vulnerability risk for blockchain smart contracts is introduced. Moreover, an effective criterion for its estimation is devised. With this criterion, to estimate the risk of an unknown smart contract, linear discriminant analysis of smart contracts and distances to their nearest neighbors were exploited. Although deep learning is not used in the proposed criterion and it requires little training data, it provides a realistic risk estimation of smart contracts. The experiments conducted on a real-world dataset of Ethereum blockchain smart contracts, including both vulnerable and safe contracts, show the acceptable performance of the proposed criterion. Moreover, the performance of the proposed criterion is superior to that of existing criteria in other areas of risk estimation.
Daniel Kwame Amissah, Winfred Yaokumah, Edward Danso Ansong, Justice Kwame Appati
ABSTRACT This study investigates the classification performance of various machine learning algorithms on the Bitcoin Heist ransomware dataset, focusing on the effects of dimensionality reduction techniques. The primary objective was to evaluate the classifiers' effectiveness in distinguishing between malicious and benign transactions under three experimental scenarios: without dimensionality reduction, utilizing Incremental Principal Component Analysis (IPCA), and applying Uniform Manifold Approximation and Projection (UMAP). The methodology involved rigorous experimentation with four classifiers: K‐Nearest Neighbors (KNN), XGBoost, Decision Tree, and Multi‐Layer Perceptron (MLP). The results demonstrated that dimensionality reduction techniques, particularly UMAP, improved the performance of KNN and Decision Tree classifiers while adversely affecting the performance of XGBoost and MLP. Notably, KNN consistently outperformed the other classifiers across different scenarios, indicating its robustness in handling reduced feature spaces. This study concludes that the effectiveness of dimensionality reduction is contingent upon the specific characteristics of the classifiers employed.
Mohammad Meezan -, Kande Vinay Karthik -, Mohammed Mehraj Pasha -, Sandhya S -
In this paper, we describe the position regulation of cryptocurrencies in India and compare it with the worldwide regulatory framework. It analyzes the influence such regulations have on the Indian market, indicating the obstacles as well as opportunities. It emphasizes the importance of focusing on such measures which both stimulate the growth of new ideas and the strength of the market as well as the security of investors. This paper aims at providing guidance on how to improve India’s approaches towards the regulation of the cryptocurrency market through a comparative perspective.
Blockchain technology, first introduced by Bitcoin in 2008, has since expanded far beyond its original scope. Its immutable ledger and decentralized nature have made it an attractive foundation for a wide range of applications, from cryptocurrencies to smart contracts and decentralized finance (DeFi). However, these same characteristics also introduce new challenges in the realm of digital forensics. Blockchain forensics involves the analysis and investigation of transactions and activities within blockchain networks to uncover fraudulent behavior, track illicit activities, and ensure compliance with regulations (Shukla, et al., 2024).
Ammad Aslam, Octavian Postolache, Sancho Oliveira, J. M. Dias Pereira
Sharding is an emerging blockchain technology that is used extensively in several fields such as finance, reputation systems, the IoT, and others because of its ability to secure and increase the number of transactions every second. In sharding-based technology, the blockchain is divided into several sub-chains, also known as shards, that enhance the network throughput. This paper aims to examine the impact of integrating sharding-based blockchain network technology in securing IoT sensors, which is further used for environmental monitoring. In this paper, the idea of integrating sharding-based blockchain technology is proposed, along with its advantages and disadvantages, by conducting a systematic literature review of studies based on sharding-based blockchain technology in recent years. Based on the research findings, sharding-based technology is beneficial in securing IoT systems by improving security, access, and transaction rates. The findings also suggest several issues, such as cross-shard transactions, synchronization issues, and the concentration of stakes. With an increased focus on showcasing the important trade-offs, this paper also offers several recommendations for further research on the implementation of blockchain network technology for securing IoT sensors with applications in environment monitoring. These valuable insights are further effective in facilitating informed decisions while integrating sharding-based technology in developing more secure and efficient decentralized networks for internet data centers (IDCs), and monitoring the environment by picking out key points of the data.
Abstract Blockchain technology has gained widespread attention and adoption in various industries. However, despite its potential benefits, there are still numerous challenges and issues that need to be addressed. This paper provides an overview of the legal, regulatory, and technical challenges related to the use of blockchain technology. It explores the challenges associated with privacy, data protection, and data security, and analyses the regulatory challenges and implications. Additionally, it identifies future challenges and issues that may arise in the field of blockchain technology, including the integration with emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data. The paper concludes by discussing the need for collaboration among stakeholders and the development of comprehensive legal and regulatory frameworks to address the challenges and ensure the successful implementation of blockchain technology in various sectors.
This research explores the impact of Non-Fungible Token (NFT) authentication on purchase intention in new and pre-loved luxury markets, grounded in warranting theory and institution-based trust theory. Using a two-study online experimental design (Study 1: new luxury market, Study 2: pre-loved luxury market), both studies used a one-factor (NFT authentication) and two-level (yes or no) design and PROCESS macro Model 6 for serial mediation analysis. The results from Study 1 indicate that NFT authentication enhances purchase intention through increased warranting value and structural assurance. Study 2 confirmed these serial mediating effects and revealed a direct significant impact of NFT authentication in the pre-loved luxury market, which was not significant in the new luxury market. This study highlights the importance of NFT authentication in enhancing consumer trust and purchase intention in both new and pre-loved luxury markets.
Abstract This study investigates the dark side of the non-fungible token (NFT) marketplace, with a focus on understanding the risks, and underlying factors driving fraud in the NFT ecosystem. Using the fraud triangle framework, this study examines pressure, opportunity, and rationalization from individual and organizational perspectives. The research provides a comprehensive understanding of the contributing factors to NFT marketplace fraud by analyzing the reasons behind fraudulent actions. A conceptual framework is developed that includes ten propositions to aid in understanding the complexity of this issue. This study’s outcomes will assist policymakers in crafting efficient approaches to mitigate fraud within the NFT marketplace.
Concerns regarding loot box fairness, along with increased exploitations of video game players and a lack of trustworthiness in video games and their merchandise, continue to rise, yet these issues have received limited attention and research. In response, this study introduces an Ethereum blockchain-based solution aimed at addressing the lack of transparency and fairness in loot box markets. Emphasizing transparency, fairness, and player trust, our model employs smart contracts to disclose reward probabilities to players, promoting informed decision-making. Central to our approach is the integration of a Random Number Generator (RNG) within the smart contract to ensure impartial outcomes. Deployed on the Sepolia testnet, designed to closely replicate the real Ethereum mainnet, our evaluation highlights improved transaction transparency and fairness. Analysis of Sepolia testnet's performance provides valuable and positive insights into blockchain operational dynamics. This paper contributes to ethical gaming practices by proposing a framework that could reshape how virtual rewards are managed, advocating for blockchain adoption to enhance gaming equity.
S M Mostaq Hossain, Amani Altarawneh, Jesse Roberts
As blockchain technology and smart contracts become widely adopted, securing them throughout every stage of the transaction process is essential. The concern of improved security for smart contracts is to find and detect vulnerabilities using classical Machine Learning (ML) models and fine-tuned Large Language Models (LLM). The robustness of such work rests on a labeled smart contract dataset that includes annotated vulnerabilities on which several LLMs alongside various traditional machine learning algorithms such as DistilBERT model is trained and tested. We train and test machine learning algorithms to classify smart contract codes according to vulnerability types in order to compare model performance. Having fine-tuned the LLMs specifically for smart contract code classification should help in getting better results when detecting several types of well-known vulnerabilities, such as Reentrancy, Integer Overflow, Timestamp Dependency and Dangerous Delegatecall. From our initial experimental results, it can be seen that our fine-tuned LLM surpasses the accuracy of any other model by achieving an accuracy of over 90%, and this advances the existing vulnerability detection benchmarks. Such performance provides a great deal of evidence for LLMs' ability to describe the subtle patterns in the code that traditional ML models could miss. Thus, we compared each of the ML and LLM models to give a good overview of each model's strengths, from which we can choose the most effective one for real-world applications in smart contract security. Our research combines machine learning and large language models to provide a rich and interpretable framework for detecting different smart contract vulnerabilities, which lays a foundation for a more secure blockchain ecosystem.
Cryptocurrencies are volatile digital currencies based on a decentralized system. Their market behavior, shaped primarily by communal factors such as developer activity and community engagement, differs from that of traditional financial instruments, which are typically driven by intrinsic factors. This study examines the impact of community engagement, as measured by developer activity on GitHub, on the valuation and trading volume of decentralized assets. A quantitative research design is used to analyze developer data from multiple cryptocurrencies. Statistical methods, including correlation analysis, are applied to assess the strength of the relationships between developer activity, asset valuation, and trading volume. Preliminary findings indicate a consistent correlation between developer engagement and both asset valuation and trading volume, offering insight into what drives the success of cryptocurrency projects. This research contributes to the rapidly growing field of cryptocurrency market analytics, highlighting developer activity as a predictive indicator and a potential tool for anticipating shifts in both market dynamics and community sentiment.
Blockchain technology, characterized by features such as decentralization, is transforming the financial system and providing new tools for financial crime governance. However, its characteristics like anonymity are also exploited by criminals, giving rise to new types of financial crime. This paper analyzes its "double-edged sword" effect from a financial professional perspective: first, it outlines the technical principles and current applications; then, it explores its empowering mechanisms as a "sharp sword" in anti-money laundering, combating terrorist financing, and enhancing transaction transparency. Subsequently, it analyzes its abuse as a "dark blade" in criminal activities such as cryptocurrency money laundering. Employing the financial regulation "trilemma" framework, the paper argues for the necessity and challenges of seeking a balance between decentralization, privacy protection, and effective regulation. It proposes comprehensive governance pathways, including building an adaptive regulatory framework that synergizes "RegTech" and "Compliance Tech." The research indicates that guiding blockchain technology to serve financial security and stability requires acknowledging and mastering its dual nature.
As a distributed shared transaction ledger, blockchain technology has the characteristics of decentralization, immutable, irreversible and traceable, and is changing the inherent model of traditional industries. Smart contracts, as one of the core applications of blockchain technology, provide the basis for a variety of practical applications. However, the frequent security problems of smart contracts have not only caused huge economic losses, but also hindered the development of blockchain systems. According to relevant studies, the economic losses caused by smart contract security breaches have exceeded billions of dollars. Therefore, the security of smart contracts has become a hot topic at home and abroad. This study discusses the technical vulnerabilities and corresponding solutions of smart contracts from the code level. In-depth analysis of security vulnerabilities in smart contracts can help identify and repair potential security hazards, improve the overall security of contracts, and prevent the theft of funds or abnormal execution of contracts. By raising the security awareness of developers, enterprises and users, it is possible to promote the application of smart contracts in high-security fields such as finance and law and promote the healthy development of the blockchain ecosystem.
This bachelor thesis focuses on the role of cryptocurrencies in the process of laundering the proceeds of crime, particularly in the context of money laundering. The aim of the thesis is to explore how cryptocurrencies, due to their anonymity and decentralized nature, facilitate the process of money laundering and circumvention of financial regulation, thus providing new opportunities for criminals to carry out illegal transactions. The theoretical part of the thesis focuses on the theoretical underpinnings of cryptocurrencies and blockchain technology that are key to their operation, and explores in detail the money laundering process and its phases. The thesis analyzes the mechanisms by which cryptocurrencies are used to mask the origin of illicit financial flows and their subsequent legalization. The practical part of the thesis includes simulations of cryptocurrency transactions using real tools to anonymise financial flows that criminals can use to launder illicit funds. At the end of the work, the weaknesses of the cryptocurrency sector regulations are identified and possible measures are proposed to reduce the risk of its abuse in illegal activities.