Abstract The burgeoning interest in decentralized applications (Dapps), spurred by advancements in blockchain technology, underscores the critical role of smart contracts. However, many Dapp users, often without deep knowledge of smart contracts, face financial risks due to hidden vulnerabilities. Traditional methods for detecting these vulnerabilities, including manual inspections and automated static analysis, are plagued by issues such as high rates of false positives and overlooked security flaws. To combat this, the article introduces an innovative approach using the bidirectional encoder representations from transformers (BERT)‐ATT‐BiLSTM model for identifying potential weaknesses in smart contracts. This method leverages the BERT pre‐trained model to discern semantic features from contract opcodes, which are then refined using a Bidirectional Long Short‐Term Memory Network (BiLSTM) and augmented by an attention mechanism that prioritizes critical features. The goal is to improve the model's generalization ability and enhance detection accuracy. Experiments on various publicly available smart contract datasets confirm the model's superior performance, outperforming previous methods in key metrics like accuracy, F1‐score, and recall. This research not only offers a powerful tool to bolster smart contract security, mitigating financial risks for average users, but also serves as a valuable reference for advancements in natural language processing and deep learning.
Reentrancy, a notorious vulnerability in smart contracts, has led to millions of dollars in financial loss. However, current smart contract vulnerability detection tools suffer from a high false positive rate in identifying contracts with reentrancy vulnerabilities. Moreover, only a small portion of the detected reentrant contracts can actually be exploited by hackers, making these tools less effective in securing the Ethereum ecosystem in practice. In this paper, we propose BlockWatchdog, a tool that focuses on detecting reentrancy vulnerabilities by identifying attacker contracts. These attacker contracts are deployed by hackers to exploit vulnerable contracts automatically. By focusing on attacker contracts, BlockWatchdog effectively detects truly exploitable reentrancy vulnerabilities by identifying reentrant call flow. Additionally, BlockWatchdog is capable of detecting new types of reentrancy vulnerabilities caused by poor designs when using ERC tokens or user-defined interfaces, which cannot be detected by current rule-based tools. We implement BlockWatchdog using cross-contract static dataflow techniques based on attack logic obtained from an empirical study that analyzes attacker contracts from 281 attack incidents. BlockWatchdog is evaluated on 421,889 Ethereum contract bytecodes and identifies 113 attacker contracts that target 159 victim contracts, leading to the theft of Ether and tokens valued at approximately 908.6 million USD. Notably, only 18 of the identified 159 victim contracts can be reported by current reentrancy detection tools.
Bitcoin and cryptocurrency adoption has increased significantly over the past few years. The significant growth in the industry has been matched by growth of crimes in this domain; not only in scams and dark-web illegal trading, but also in white-collar crimes with fraud and perjury occurring increasingly. With blockchain technology, the world of financial infidelity has become increasingly sophisticated. There is a common belief that blockchain and cryptocurrency provide means of hiding funds from the public or close associates who may not be familiar with the technology. The rise of cryptocurrency has also led to spouses hiding digital assets during divorce settlements. This study presents a use case of a couple in the midst of a divorce where one of the spouses was accused of perjury for failure to declare bitcoin holdings, obtained via Bitcoin mining, and possibly other forms of cryptocurrency and digital assets to the court. The plaintiff is entitled to fifty percent of all assets. While property, stocks, bonds, and bank accounts can easily be traced, cryptocurrency assets are more complex to trace but it is not impossible. This paper illustrates how such a case can be investigated by following the flow of funds on the blockchain, using tools such as Maltego and QLUE. The paper thus presents an investigative process that can be followed for a new category of forensic investigation.
Cryptocurrency is a new thing that descended on this world, as it began to be very popular and in demand. It has been circulated among societies since the past year or shortly before, although its establishment was announced in 2009 by an unknown party that called itself “Satoshi Nakamoto”. The research problem lies in the emergence of these encrypted digital currencies the profit seekers hasten to acquire, promote, and sell them. The research attempts to answer six questions; Including: What is the truth about encrypted digital currencies? What is the current jurisprudential adaptation to it? This research aims to highlight its reality, the history of its emergence, its characteristics, and the provisions for dealing with it. on the descriptive analytical approach as well as the historical approach, and this research contains six demands; The first: what are the encrypted digital currencies, the second: is the importance of currencies in human life, the third: is the history of the emergence of encrypted digital currencies, the fourth: jurisprudential controls for the issuance of currencies, and the fifth: the characteristics of encrypted digital currencies. Sixth: the ruling on buying and selling digital currencies coded and, dealing with it, and these demands followed a conclusion that included its results, the most important of which are: currencies are the sovereign function of states, and encrypted digital currencies without state recognition is not valid relying on it because of the deception and dangers it entails, and Islam forbids that, and this ruling will be removed the adoption of countries for it, or at least the country of origin, and if its finances disappear, it is not correct to deal with it where it is correct dealing in paper currencies, and if its finance is proven, it is correct to deal with it, as it is correct to deal in paper currencies.
P. Maragathavalli, Aravindhar RS, R Keerthana, M. Harini · 5 authors
Cybercrime gives challenges to law enforcement agencies to secure digital evidence and maintain its integrity. Blockchain known for its decentralized and immutable nature, provides a secure ledger to record digital evidence transactions restricting unauthorized access. This project proposes a framework for digital forensic evidence management, contributing to the enhancement of security and reliability in digital forensic practices through the utilization of Ethereum Blockchain technology and Advanced Encryption Standard (AES) encryption. Through a systematic review, various studies, methodologies, and implementations employing Blockchain to safeguard digital evidence are explored. Blockchain, known for its decentralized and immutable nature, provides a secure ledger to record digital evidence transactions, restricting unauthorized access. Advanced Encryption Standard (AES) algorithm ensures that the data stored on the blockchain remains tamper-resistant and secure. In the blockchain ecosystem, Proof of Stake (POS) plays a critical role by facilitating transaction validation and block creation. It distinguishes itself by selecting validators based on the amount of cryptocurrency they 'stake' or pledge as collateral, offering an energy-efficient and environmentally sustainable alternative to the traditional method.
Munir Ahmad, Muhammad Idrees, Muhammad Saleem Qazi
Cryptocurrencies have become a preferred mode of terror financing. it is imperative to analyze the role of cryptocurrencies in empowering terrorist organizations especially in developing states. It takes on a more serious form in developing countries like Pakistan, which is fighting a battle against terrorism for the last few decades. Now digital financing has become an undeniable reality of the modern world, it is essential to devise a comprehensive strategy for breaking the nexus between terrorism and cryptocurrencies. This article intends to explore the answers to the questions that why cryptocurrencies are becoming a preferred mode for terror financing. It argues that the anonymity, decentralization and operational ease of cryptocurrencies make them a preferred medium for terrorist organizations. Therefore, enhancing institutional capacity by training human resources and making dedicated efforts at the national level will prove to be instrumental in delinking terrorism and cryptocurrencies. Moreover, close collaboration between international watchdogs and national institutions is essential in mitigating the threat of the use of cryptocurrencies for terrorism.
Collaborative cybersecurity relies on organizations sharing information to boost security, but trust management is a key concern. Decentralized solutions like distributed ledgers, particularly blockchain, are crucial for eliminating single points of failure. However, the existing literature on blockchain-based collaborative cybersecurity is limited, lacking comprehensive insights. This paper addresses this gap by surveying blockchain's role in collaborative cybersecurity from 2016 to 2023. It explores various applications, trends, and the evolution of blockchain technology, focusing on access control, data validation policies, underlying tech, and consensus mechanisms. A key finding is the fragmentation of the field with no dominant research group or venue. Many recent projects poorly select consensus protocols for their blockchain. To aid researchers and practitioners, this paper offers guidelines for choosing the right blockchain for specific purposes and highlights open research areas and lessons learned from past blockchain applications in collaborative cybersecurity, encouraging further exploration in this field.
Abstract This study focuses on the analysis of cryptocurrency regulatory developments in Europe. The aim is to review national crypto-legislation in Europe and the EU's latest initiative to create designated regulatory instruments for the crypto-economy. This study assessed whether the European Union's Regulation on Markets in Crypto-Assets (MiCA) would have the intended effect. Drawing on the results of a survey of crypto experts from five European countries, this study evaluated the effectiveness of current regulation across Europe and how it can be improved to reduce financial crimes. The findings show that a unified national legal framework for regulating transactions with crypto assets does not exist in European countries. Current crypto regulations are dictated by anti-money laundering recommendations. This study provides suggestions for improving MiCA regulation. The article offers recommendations for an international regulatory standard for crypto assets and insights for increasing efficiency in regulating DeFi, NFTs, and smart contracts.
Cyber Threat Intelligence (CTI) has become increasingly important in safeguarding organizations against cyber threats. However, managing, storing, analyzing, and sharing vast and sensitive threat intelligence data is a challenge. Blockchain technology, with its robust and tamper-resistant properties, offers a promising solution to address these challenges. This systematic literature review explores the recent advancements and emerging trends at the intersection of CTI and blockchain technology. We reviewed research papers published during the last 5 years to investigate the various proposals, methodologies, models, and implementations related to the distributed ledger technology and how this technology can be used to collect, store, analyze, and share CTI in a secured and controlled manner, as well as how this combination can further support additional dimensions such as quality assurance, reputation, and trust. Our findings highlight the focus of the CTI and blockchain convergence on the dissemination phase in the CTI lifecycle, reflecting a substantial emphasis on optimizing the efficacy of communication and sharing mechanisms, based on an equitable emphasis on both permissioned, private blockchains and permissionless, public blockchains, addressing the diverse requirements and preferences within the CTI community. The analysis reveals a focus towards the tactical and technical dimensions of CTI, compared to the operational and strategic CTI levels, indicating an emphasis on more technical-oriented utilization within the domain of blockchain technology. The technological landscape supporting CTI and blockchain integration emerges as multifaceted, featuring pivotal roles played by smart contracts, machine learning, federated learning, consensus algorithms, IPFS, deep learning, and encryption. This integration of diverse technologies contributes to the robustness and adaptability of the proposed frameworks. Moreover, our exploration unveils the overarching significance of trust and privacy as predominant themes, underscoring their pivotal roles in shaping the landscape within our research realm. Additionally, our study addresses the maturity assessment of these integrated systems. The approach taken in evaluating maturity levels, distributed across the Technology Readiness Level (TRL) scale, reveals an average balance, indicating that research efforts span from early to mid-stages of maturity in implementation. This study signifies the ongoing evolution and maturation of research endeavors within the dynamic intersection of CTI and blockchain technology, identifies trends, and also highlights research gaps that can potentially be addressed by future research on the field.
In the introductory part of the paper, the author briefly explores the emergence of the first cryptocurrency (Bitcoin), which was initially devised for the purpose of securing easier transactions without intermediaries. Criminals soon realised that cryptocurrencies, due to their inherent characteristics, could provide them with anonymity. As other cryptocurrencies (altcoins) emerged, it was necessary to define their conceptual framework. While cryptocurrencies were initially used in illegal sales of narcotics, their application soon spread to a number of other criminal activities. In that context, the author first presents the reasons that led criminals to turn to cryptocurrencies in their financial transactions, and then explains the possible uses of cryptocurrencies in the commission of crime. The central part of the paper provides examples of criminal activities committed by using cryptocurrencies. It is reasonable to expect that, in the future, the use of cryptocurrencies will extend to other criminal activities, which are still unaffected by the trend that has existed for the last ten years.
A smart contract is a computerised transaction agreement that carries out predefined terms without human involvement or third-party intermediaries. It serves as a trust intermediary in several industries, including finance, insurance, and supply chain management, in the blockchain 2.0 era. With the increasing interest in smart contracts, security has become a serious problem. Examining typical vulnerability types and vulnerability detection methodologies is of special importance. In this research, a comprehensive evaluation of common smart contract security vulnerabilities is conducted, and a three-tier threat model is then provided to classify the vulnerabilities. In addition, we examine fourteen existing smart contract analysis tools for finding vulnerabilities and classify them according to the main technique they apply. This article is designed to serve as a reference for people who wish to analyse deployed code and enhance existing detection techniques. At the conclusion, open issues and future research paths regarding smart contract vulnerability detection are presented.
Open access
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Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The rapid growth in the fintech industry propels financial services into the digital era, bringing unprecedented convenience and efficiency. However, this transformation could be a smoother process; it faces difficulties, primarily in Cybersecurity. This comprehensive study explores the cybersecurity landscape in the fintech industry, including common threats, existing defensive measures, and innovative solutions that shape the future. Significant dangers, such as data breaches, phishing attacks, and malware complications, emphasize the need for strong cybersecurity strategies. Fintech firms address these concerns by employing various defensive measures, including encryption technology, robust multi-factor authentication, and strict compliance with legal frameworks. Examining prospects, the article explores emerging themes such as the mysterious domain of quantum-resistant cryptography, the mysterious frontier of behavioral analytics, and the shift toward decentralized identification solutions. These developments demonstrate a proactive shift in strategy, anticipating and preparing for potential hazards to prevent and minimize their impact. The conclusion presents important findings, drawing out their implications for the future and proposing sensible suggestions for further research and industrial practices. This research provides vital insights for stakeholders in the rapidly changing field of fintech, helping them navigate the complex intersection of finance and technology and guaranteeing a safe journey through unfamiliar areas.
Smart contracts are becoming appealing targets for hackers because of the vast amount of cryptocurrencies under their control. Asset loss due to the exploitation of smart contract codes has increased significantly in recent years. To guarantee that smart contracts are vulnerability-free, there are many works to detect the vulnerabilities of smart contracts, but only a few vulnerability repair works have been proposed. Repairing smart contract vulnerabilities at the source code level is attractive as it is transparent to users, whereas existing repair tools, such as SCRepair and sGuard , suffer from many limitations: (1) ignoring the code of vulnerability prevention; (2) possibly applying the repair to the wrong statements and changing the original business logic of smart contracts; and (3) showing poor performance in terms of time and gas overhead. In this work, we propose machine learning guided rule-based automated vulnerability repair on smart contracts to improve the effectiveness and efficiency of sGuard . To address the limitations mentioned above, we design the features that characterize both the symptoms of vulnerabilities and the methods of vulnerability prevention to learn various vulnerability patterns and reduce false positives. Additionally, a fine-grained localization algorithm is designed by traversing the nodes of the abstract syntax tree, and we refine and extend the repair rules of sGuard to preserve the original business logic of smart contracts and support new vulnerability types. Our tool, named sGuard+ , reduces time overhead based on machine learning models, and reduces gas overhead by fewer code changes and precise patching. In our experiment, we collect a publicly available vulnerability dataset from CVE, SWC, and SmartBugs Curated as a ground truth for evaluations. Overall, sGuard+ repairs more vulnerabilities with less time and gas overhead than state-of-the-art tools. Furthermore, we reproduce about 9,000 historical transactions for regression testing. It is shown that sGuard+ has no impact on the original business logic of smart contracts.
Our daily activities revolve around various technologies, the smart city technologies and the services they offer have all influenced modern living conditions. The purpose of a smart city is to enhance people's quality of life and provide possibilities to address social and environmental issues. As a developing technology, blockchain is beneficial for enhancing smart city services including food tracking, supply-demand matching, the security of connected cars, and regulatory compliance. We propose a new framework for describing how blockchain technology is used in smart contracts to improve security, dependability, and many other positive outcomes in a smart city environment. We propose smarter and resilient smart contracts using blockchain technology to manage real estate information. We propose a framework with tamperproof functionality to store the data and retrieve the data. Smart contract mathematical computations like overhead rate, execution time, mean computational cost, standard deviation, throughput, and resource utilization are evaluated and the results are compared. This paper focuses on real-world rental file management scenarios to demonstrate the benefits of blockchain technology and how it is used to address the issues that currently exist in developing smart contracts and services while exchanging real land and other properties in a smart city environment.
This research introduces innovative features tailored to capture distinctive characteristics of ransomware activity within the cryptocurrency ecosystem. The study employs a multifaceted analysis to delve into ransomware-related data encompassing transaction metadata, ransom analysis, behavioral patterns, and financial aspects. A feature selection algorithm is explored to discern ransomware transactions in Bitcoin (BTC) and the United States Dollar (USD) using the UGRansome dataset. This comprehensive dataset of ransomware-related transactions facilitates the proposal of novel features designed to capture the unique traits of ransomware activity. The correlation matrix and temporal analysis of these features contribute to a nuanced understanding of the dynamic nature of ransomware threats. The research presents the Ransomware Feature Selection Algorithm (RFSA) based on Gini Impurity and Mutual Information (MI) to effectively select crucial ransomware features. Evaluation metrics such as precision, recall, accuracy, and F1 score highlight the effectiveness of the RFSA. The analysis reveals that approximately 68% of ransomware incidents involve BTC transactions ranging from 1.46 to 2.56, with an average of 2.01 BTC transactions per attack. Moreover, ransomware causes financial damages ranging from 4.38 to 172.36 USD, with an average damage of 88.37 USD. The RFSA identifies 17 ransomware types and their associated malware to shed light on their characteristics. The study investigates the pricing of ransomware and reveals that TowerWeb is associated with the highest fee, amounting to 135.26 BTC, while CryptoLocker has the lowest fee, recorded at 10.51 BTC. Additionally, the impact of ransomware duration on financial gains and network flow is investigated, disclosing a correlation between extended duration and higher financial gains. The research achieves outstanding performance metrics, including an MI score of 95%, accuracy of 93%, recall of 92%, and precision of 89%. These results showcase the superiority of the proposed approach over existing studies, emphasizing the dynamic and adaptable nature of ransomware demands. The findings suggest that there is no fixed amount for specific cyberattacks. This underscores the importance of adapting to the evolving landscape of ransomware threats.
Ensuring robust security in the Internet of Things (IoT) landscape is of paramount importance. This research article presents a novel approach to enhance IoT security by leveraging collaborative threat intelligence and integrating blockchain technology with machine learning (ML) models. The iOS application acts as a central control centre, facilitating the reporting and sharing of detected threats. The shared threat data is securely stored on a blockchain network, enabling ML models to access and learn from a diverse range of threat scenarios. The research focuses on implementing Random Forest, Decision Tree classifier, Ensemble, LSTM, and CNN models on the IoT23 dataset within the context of a Collaborative Threat Intelligence Framework for IoT Security. Through an iterative process, the models’ accuracy is improved by reducing false negatives through the collaborative threat intelligence system. The article investigates the implementation details, privacy considerations, and the seamless integration of ML-based techniques for continuous model improvement. Experimental evaluations on the IoT23 dataset demonstrate the effectiveness of the proposed system in enhancing IoT security and mitigating potential threats. The research contributes to the advancement of collaborative threat intelligence and blockchain technology in the context of IoT security, paving the way for more secure and reliable IoT deployments.
Blockchain, as one of the emerging technologies in recent years, is essentially a decentralized distributed ledger. By leveraging blockchain, it provides a new approach to identity authentication. This paper provides an overview of the applications of identity authentication based on blockchain. Firstly, the background knowledge of blockchain is introduced. Then, the technical aspects of identity authentication based on blockchain are discussed and classified from the perspectives of identity authentication techniques and cryptographic algorithms. Subsequently, the applications of identity authentication based on blockchain technology are introduced and classified in various fields. Finally, a summary of the entire paper is presented.
Elohim Fonseca dos Reis, Alexander Teytelboym, Abeer ElBahrawy, Ignacio De Loizaga · 5 authors
Dark web marketplaces have been a significant outlet for illicit trade, serving millions of users worldwide for over a decade. However, not all users are the same. This paper aims to identify the key players in Bitcoin transaction networks linked to dark markets and assess their role by analysing a dataset of 40 million Bitcoin transactions involving the 31 major markets in the period 2011-2021. First, we propose an algorithm that categorizes users either as buyers or sellers, and show that a large fraction of the trading volume is concentrated in a small group of elite market participants. We find that the dominance of markets is reflected in trading properties of buyers and sellers. Then, we investigate both market star-graphs and user-to-user networks, and highlight the importance of a new class of users, namely 'multihomers', who operate on multiple marketplaces concurrently. Specifically, we show how the networks of multihomers and seller-to-seller interactions can shed light on the resilience of the dark market ecosystem against external shocks. Our findings suggest that understanding the behavior of key players in dark web marketplaces is critical to effectively disrupting illegal activities.
With the ever-increasing advancement in blockchain technology, security is a significant concern when substantial investments are involved. This paper explores known smart contract exploits used in previous and current years. The purpose of this research is to provide a point of reference for users interacting with blockchain technology or smart contract developers. The primary research gathered in this paper analyses unique smart contracts deployed on a blockchain by investigating the Solidity code involved and the transactions on the ledger linked to these contracts. A disparity was found in the techniques used in 2021 compared to 2023 after Ethereum moved from a Proof-of-Work blockchain to a Proof-of-Stake one, demonstrating that with the advancement in blockchain technology, there is also a corresponding advancement in the level of effort bad actors exert to steal funds from users. The research concludes that as users become more wary of malicious smart contracts, bad actors continue to develop more sophisticated techniques to defraud users. It is recommended that even though this paper outlines many of the currently used techniques by bad actors, users who continue to interact with smart contracts should consistently stay up to date with emerging exploitations.
Alejandro Valencia-Arías, Juan David González-Ruíz, Lilian Verde Flores, Luis Vega-Mori · 6 authors
Machine learning and blockchain technology are fast-developing fields with implications for multiple sectors. Both have attracted a lot of interest and show promise in security, IoT, 5G/6G networks, artificial intelligence, and more. However, challenges remain in the scientific literature, so the aim is to investigate research trends around the use of machine learning in blockchain. A bibliometric analysis is proposed based on the PRISMA-2020 parameters in the Scopus and Web of Science databases. An objective analysis of the most productive and highly cited authors, journals, and countries is conducted. Additionally, a thorough analysis of keyword validity and importance is performed, along with a review of the most significant topics by year of publication. Co-occurrence networks are generated to identify the most crucial research clusters in the field. Finally, a research agenda is proposed to highlight future topics with great potential. This study reveals a growing interest in machine learning and blockchain. Topics are evolving towards IoT and smart contracts. Emerging keywords include cloud computing, intrusion detection, and distributed learning. The United States, Australia, and India are leading the research. The research proposes an agenda to explore new applications and foster collaboration between researchers and countries in this interdisciplinary field.
Elena Baninemeh, Slikker, Marre, Katsiaryna Labunets, Jansen, Slinger
Distributed ledger technologies have gained significant attention and adoption in recent years. Despite various security features distributed ledger technology provides, they are vulnerable to different and new malicious attacks, such as selfish mining and Sybil attacks. While such vulnerabilities have been investigated, detecting and discovering appropriate countermeasures still need to be reported. Cybersecurity knowledge is limited and fragmented in this domain, while distributed ledger technology usage grows daily. Thus, research focusing on overcoming potential attacks on distributed ledgers is required. This study aims to raise awareness of the cybersecurity of distributed ledger technology by designing a security risk assessment method for distributed ledger technology applications. We have developed a database with possible security threats and known attacks on distributed ledger technologies to accompany the method, including sets of countermeasures. We employed a semi-systematic literature review combined with method engineering to develop a method that organizations can use to assess their cybersecurity risk for distributed ledger applications. The method has subsequently been evaluated in three case studies, which show that the method helps to effectively conduct security risk assessments for distributed ledger applications in these organizations.
Bhupendra Acharya, Muhammad Saad, Antonio Emanuele Ciná, Lea Schönherr · 8 authors
The mainstream adoption of cryptocurrencies has led to a surge in wallet-related issues reported by ordinary users on social media platforms. In parallel, there is an increase in an emerging fraud trend called cryptocurrency-based technical support scam, in which fraudsters offer fake wallet recovery services and target users experiencing wallet-related issues.In this paper, we perform a comprehensive study of cryptocurrency-based technical support scams. We present an analysis apparatus called HoneyTweet to analyze this kind of scam. Through HoneyTweet, we lure over 9K scammers by posting 25K fake wallet support tweets (so-called honey tweets). We then deploy automated systems to interact with scammers to analyze their modus operandi. In our experiments, we observe that scammers use Twitter as a starting point for the scam, after which they pivot to other communication channels (e.g., email, Instagram, or Telegram) to complete the fraud activity. We track scammers across those communication channels and bait them into revealing their payment methods. Based on the modes of payment, we uncover two categories of scammers that either request secret key phrase submissions from their victims or direct payments to their digital wallets. Furthermore, we obtain scam confirmation by deploying honey wallet addresses and validating private key theft. We also collaborate with the prominent payment service provider by sharing scammer data collections. The payment service provider feedback was consistent with our findings, thereby supporting our methodology and results. By consolidating our analysis across various vantage points, we provide an end-to-end scam lifecycle analysis and propose recommendations for scam mitigation.
Abstract Identifying illicit behavior in the Bitcoin network is a well‐explored topic. The methods proposed over time have generated great insights into the deanonymization of the Bitcoin user base through the clustering of inputs and outputs. With advanced techniques being deployed by Bitcoin users, these heuristics are now being challenged in their ability to aid in the detection of illicit activity. In this paper, we provide a comprehensive list of methods deployed by malicious actors on the network and illicit transaction mining methods. We detail the evolution of the heuristics that are used to deanonymize Bitcoin transactions. We highlight the issues associated with conducting law enforcement investigations and propose recommendations for the research community to address these issues. Our recommendations include the release of public data by exchanges to allow researchers and law enforcement to further protect the network from malicious users. We recommend the enhancement of current heuristics through machine learning methods and discuss how researchers can take the fight head‐on against expert cybercriminals.
Blockchain technology offers a promising way to improve business processes by providing a secure and transparent transaction platform. However, using this technology brings its own set of challenges, especially when trying to balance user privacy with legal and regulatory needs. This article explores the challenges of keeping user information private, adhering to regulatory frameworks, and fulfilling legal requirements on the blockchain. A key point in this research is the challenge of keeping or maintaining confidentiality while being transparent. The article also discusses the issues of applying legal rules to a system not controlled by one central authority, the risks of privacy and security breaches, and the need to follow data protection laws. The article highlights how some blockchain-based companies have tackled these challenges, mainly through smart blockchain management and innovative technology, by looking at real-world examples from major companies like IBM, Bitpay, Ripple, and Coinbase. The systematic literature review (SLR) methodology involved reviewing literature from the past 15 years (2008-2023) from trusted sources like Google Scholar, ACM Digital Library, IEEE, Springer, and Science Direct. The findings indicate that cutting-edge technologies prioritizing privacy, such as zero-knowledge proofs, ring signatures, and encryption methods, would enable Bitcoin (BTC) platform operations to maintain or balance privacy and transparency. Furthermore, the study indicates the importance of clear privacy guidelines, adhering to relevant regulations, working closely with regulators and law enforcement, and educating users. In summary, it is crucial to approach blockchain carefully, prioritizing user privacy while meeting all legal and regulatory requirements.