Purpose The purpose of this paper is to provide an extensive examination and analysis of the current literature on the use of blockchain technology in courts. The paper aims to explore the potential benefits of implementing blockchain technology in courts, such as increasing transparency and accountability, improving the efficiency of court procedures and enhancing the security of court records. Additionally, the paper intends to identify the challenges and limitations of using blockchain technology in courts and propose potential solutions to overcome these obstacles. The ultimate goal is to provide a comprehensive understanding of the potential applications and implications of blockchain technology in the context of the court system. Design/methodology/approach The research design of this study is qualitative, involving a thorough examination and analysis of existing literature on the use of blockchain technology in courts. The data collection procedure involves gathering information from various sources, such as academic publications, official reports and other relevant records. Data analysis is conducted using a thematic analysis approach, which identifies and categorizes recurring themes that emerge from the data. This approach ensures that the results are credible, dependable and accurate representations of the experiences of the participants. By using these methodologies, the study is able to draw meaningful conclusions and insights into the use of blockchain technology in courts. Findings The major findings of this paper suggest that the implementation of blockchain technology in courts has the potential to bring significant benefits such as increased transparency, efficiency and security. The use of blockchain technology in courts can enable the creation of tamper-proof records that are immutable, secure and transparent, which can help prevent fraud, reduce costs and enhance trust in the judicial system. However, adopting this technology also poses challenges and limitations, such as interoperability, governance and scalability. Overall, the paper concludes that while there are challenges to be addressed, the benefits of blockchain technology in courts are significant and should be explored further. Research limitations/implications The study has several limitations that need to be taken into account. Firstly, the availability of data on blockchain implementation in the court system is limited, making it challenging to provide a comprehensive analysis of the topic. Thus, the study’s findings may not be generalizable to other contexts. Secondly, the study takes a technology-centric approach and does not consider blockchain technology’s social and legal implications in court operations. Thirdly, the case studies presented in this paper are limited to a few countries. Moreover, the implementation of blockchain technology in the court system is still in its early stages and lacks standardization, technical expertise and regulatory frameworks. Lastly, uncertainty around the legal framework may hinder its widespread adoption and use. Practical implications The practical implications of this study suggest that the use of blockchain technology in courts has the potential to improve efficiency, security, transparency and accountability in the court system. It can reduce the risk of data tampering, expedite case resolution and lower the cost of legal proceedings. Therefore, this study provides a framework for courts to consider blockchain technology’s potential benefits and explore its future adoption. Social implications The social implications of this study are significant, as the adoption of blockchain technology in the court system can have a profound impact on society. Firstly, by increasing transparency and accountability, blockchain technology can promote public trust in the court system and improve access to justice, particularly for disadvantaged communities (Liu et al. , 2020). Secondly, blockchain technology can reduce the reliance on intermediaries, such as lawyers, and streamline the case management process, making legal services more accessible and affordable for the general public (Khurana, 2020). Finally, the use of blockchain technology can create a more secure and efficient court system, enhancing the overall effectiveness of the judicial system and promoting public confidence. Originality/value This study provides an original contribution to the literature by exploring the use of blockchain technology in courts from a qualitative research design perspective. While there are a growing number of studies on the potential applications of blockchain technology in various fields, this study provides a comprehensive examination of the current literature on the use of blockchain in courts, identifying the benefits and limitations of its implementation. The study’s focus on the strengths and limitations of blockchain technology and its implications in court adds to the originality of this research.
As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniques. This paper aims to provide a comprehensive understanding of the fundamental principles underlying cybersecurity in WSNs. In addition to current and envisioned solutions that have been studied in detail, this review primarily focuses on state-of-the-art Machine Learning (ML) and Blockchain (BC) security techniques by studying and analyzing 164 up-to-date publications highlighting security aspect in WSNs. Then, the paper discusses integrating BC and ML towards developing a lightweight security framework that consists of two lines of defence, i.e, cyberattack detection and cyberattack prevention in WSNs, emphasizing the relevant design insights and challenges. The paper concludes by presenting a proposed integrated BC and ML solution highlighting potential BC and ML algorithms underpinning a less computationally demanding solution.
Xingbang Ma, Dongsheng Yu, Yanhui Du, Lanting Li · 6 authors
With the development of the Internet, cyberattacks are becoming increasingly complex, sustained, and organized. Cyber threat intelligence sharing is one of the effective ways to alleviate the pressure on organizational or individual cyber security defense. However, the current cyber threat intelligence sharing lacks effective incentive mechanisms, resulting in mutual distrust and a lack of motivation to share among sharing members, making the security of sharing questionable. In this paper, we propose a blockchain-based cyber threat intelligence sharing mechanism (B-CTISM) to address the problems of free riding and lack of trust among sharing members faced in cyber threat intelligence sharing. We use evolutionary game theory to analyze the incentive strategy; the resulting evolutionarily stable strategy achieves the effect of promoting sharing and effectively curbing free-riding behavior. Then, the incentive strategy is deployed to smart contracts running in the trusted environment of blockchain, whose decentralization and tamper-evident properties can provide a trusted environment for participating members and establish trust without a third-party central institution to achieve secure and efficient cyber threat intelligence sharing. Finally, the effectiveness of the B-CTISM in facilitating and regulating threat intelligence sharing is verified through experimental simulation and comparative analysis.
Permissionless blockchains promise to be resilient against censorship by a single entity. This suggests that deterministic rules, and not third-party actors, are responsible for deciding if a transaction is appended to the blockchain or not. In 2022, the U.S. Office of Foreign Assets Control (OFAC) sanctioned a Bitcoin mixer and an Ethereum application, putting the neutrality of permissionless blockchains to the test. In this paper, we formalize quantify and analyze the security impact of blockchain censorship. We start by defining censorship, followed by a quantitative assessment of current censorship practices. We find that 46% of Ethereum blocks were made by censoring actors that intend to comply with OFAC sanctions, indicating the significant impact of OFAC sanctions on the neutrality of public blockchains. We further uncover that censorship not only impacts neutrality, but also security. We show how after Ethereum's move to Proof-of-Stake (PoS) and adoption of Proposer-Builder Separation (PBS) the inclusion of censored transactions was delayed by an average of 85%. Inclusion delays compromise a transaction's security by, e.g., strengthening a sandwich adversary. Finally we prove a fundamental limitation of PoS and Proof-of-Work (PoW) protocols against censorship resilience.
Blockchain provides the unique and accountable channel for financial forensics by mining its open and immutable transaction data. A recent surge has been witnessed by training machine learning models with cryptocurrency transaction data for anomaly detection, such as money laundering and other fraudulent activities. This paper presents a holistic applied data science approach to fraud detection in the Bitcoin network with two original contributions. First, we contribute the Elliptic++dataset, which extends the Elliptic transaction dataset to include over 822k Bitcoin wallet addresses (nodes), each with 56 features, and 1.27M temporal interactions. This enables both the detection of fraudulent transactions and the detection of illicit addresses (actors) in the Bitcoin network by leveraging four types of graph data: (i) the transaction-to-transaction graph, representing the money flow in the Bitcoin network, (ii) the address-to-address interaction graph, capturing the types of transaction flows between Bitcoin addresses, (iii) the address-transaction graph, representing the bi-directional money flow between addresses and transactions (BTC flow from input address to one or more transactions and BTC flow from a transaction to one or more output addresses), and (iv) the user entity graph, capturing clusters of Bitcoin addresses representing unique Bitcoin users. Second, we perform fraud detection tasks on all four graphs by using diverse machine learning algorithms. We show that adding enhanced features from the address-to-address and the address-transaction graphs not only assists in effectively detecting both illicit transactions and illicit addresses, but also assists in gaining in-depth understanding of the root cause of money laundering vulnerabilities in cryptocurrency transactions and the strategies for fraud detection and prevention. The Elliptic++ dataset is released at https://www.github.com/git-disl/EllipticPlusPlus.
The wide application of Ethereum smart contracts in the Internet of Things, finance, medical, and other fields is associated with security challenges. Traditional detection methods detect vulnerabilities by stacking hard rules, which are associated with the bottleneck of a high false-positive rate and low detection efficiency. To make up for the shortcomings of traditional methods, existing deep learning methods improve model performance by combining multiple models, resulting in complex structures. From the perspective of optimizing the model feature space, this study proposes a vulnerability detection scheme for Ethereum smart contracts based on metric learning and a bidirectional long short-term memory (BiLSTM) network. First, the source code of the Ethereum contract is preprocessed, and the word vector representation is used to extract features. Secondly, the representation is combined with metric learning and the BiLSTM model to optimize the feature space and realize the cohesion of similar contracts and the discreteness of heterogeneous contracts, improving the detection accuracy. In addition, an attention mechanism is introduced to screen key vulnerability features to enhance detection observability. The proposed method was evaluated on a large-scale dataset containing four types of vulnerabilities: arithmetic vulnerabilities, re-entrancy vulnerabilities, unchecked calls, and inconsistent access controls. The results show that the proposed scheme exhibits excellent detection performance. The accuracy rates reached 88.31%, 93.25%, 91.85%, and 90.59%, respectively.
Marzooq Hadi Marzooq Alyami, Reem Alhotaylah, Sawsan Alshehri, Abdullah Alghamdi
With the rapid development of technology in all fields, including the financial field, people have flocked to invest in cryptocurrencies, sometimes without prior knowledge or experience. This has prompted hackers to prey on inexperienced investors through many types of fraud and attacks, especially phishing attacks. Cryptocurrency investment transactions take place without intermediaries such as banks and monetary institutions. Investing in cryptocurrencies is a form of peer-to-peer transaction and takes place without the involvement of physical wallets. This study addresses cases where people may become victims of phishing attacks due to the nature of cryptocurrency investments. The aim of this study was to understand the concepts of various phishing attacks on cryptocurrencies and to measure the awareness of cryptocurrency investors in the Arab Gulf countries regarding the security risks associated with cryptocurrency investments. This research was conducted by distributing a questionnaire among cryptocurrency investors and collecting and analyzing all the survey responses. The results reveal a lack of awareness about how to deal with the security risks associated with cryptocurrency investments. The research concludes that the majority of cryptocurrency investors are unaware of how to deal with phishing attacks. Finally, we address future research directions and recommend actions that can be taken to increase investors’ awareness of this issue.
Indhumathi Chandrasekeran, A. Dharmaraj, Ashima Juyal, M. Shravan · 6 authors
Cryptocurrency and data privacy are two important considerations in human resource management. Cryptocurrency, such as Bitcoin, can be used to pay employees, provide incentives, and manage payroll, but it also poses risks such as price volatility and regulatory issues. Data privacy, on the other hand, is the protection of personal information from unauthorized access and use. The use of blockchain technology, which is the underlying technology behind many cryptocurrencies, can enhance data privacy in human resource management by providing a secure and tamper-proof record of all transactions. However, it’s important to comply with all applicable laws and regulations related to the use of cryptocurrency and data privacy in human resource management.
Mining attacks allow adversaries to obtain a disproportionate share of the mining reward by deviating from the honest mining strategy in the Bitcoin system. Among them, the most well-known are selfish mining (SM), block withholding (BWH), fork after withholding (FAW) and bribery mining. In this paper, we propose two novel mining attacks: bribery semi-selfish mining (BSSM) and bribery stubborn mining (BSM). Both of them can increase the relative extra reward of the adversary and will make the target bribery miners suffer from the bribery miner dilemma. All targets earn less under the Nash equilibrium. For each target, their local optimal strategy is to accept the bribes. However, they will suffer losses, comparing with denying the bribes. Furthermore, for all targets, their global optimal strategy is to deny the bribes. Quantitative analysis and simulation have been verified our theoretical analysis. We propose practical measures to mitigate more advanced mining attack strategies based on bribery mining, and provide new ideas for addressing bribery mining attacks in the future. However, how to completely and effectively prevent these attacks is still needed on further research.
Bitcoin is the first decentralized peer-to-peer cryptocurrency that has gained popularity by providing users with transaction anonymity. With the development of Bitcoin and the higher privacy requirements of users, mixing services have emerged to enhance Bitcoin anonymity by obfuscating the flow of funds. However, they are also widely used for illegal activities due to its strong anonymity, especially for money laundering. Therefore, detecting mixing services has great significance for Bitcoin anti-money laundering. In this article, we propose a novel detection scheme to identify the addresses belonging to Bitcoin mixing services. Specifically, we first construct the Bitcoin mixing data set, which summarizes a total of 26 features to describe the transaction behavior of addresses. Next, we design a new classification model, called the Dual Ensemble Classification Model. The model combines the advantages of multiple models based on different algorithms and obtains better classification performance. In order to detect more complex mixing patterns, we also extract transaction subgraphs from the established Bitcoin address-transaction network. The subgraphs are then classified using a kernel-based graph classification method, which is embedded in the model. Comprehensive experiments on three data sets demonstrate the effectiveness of our scheme, and the proposed model has a detection accuracy of 99.84% for the Bitcoin mixing service.
The first cryptocurrency was invested in 2008/09, but the Blockchain-Web3 concept is still in its infancy, and the cyber risk is constantly changing. Our cybersecurity should also be adapting to these changes to ensure security of personal data and continuation of business for organisations. This review paper starts with a comparison of existing cybersecurity standards and regulations from the National Institute of Standards and Technology (NIST) and the International Organization for Standardization (ISO) - ISO27001, followed by a discussion on more specific and recent standards and regulations, such as the Markets in Crypto-Assets Regulation (MiCA), Committee on Payments and Market Infrastructures and the International Organisation of Securities Commissions (CPMI-IOSCO), and more general cryptography and post-quantum cryptography, in the context of cybersecurity. These topics are followed up by a review of recent technical reports on cyber risk/security and a discussion on cloud security questions. Comparison of Blockchain cyber risk is also performed on the recent EU standards on cyber security, including European Cybersecurity Certification Scheme (EUCS) – cloud, and additional US standards – The National Vulnerability Database (NVD) Common Vulnerability Scoring System (CVSS). The study includes a review of Blockchain endpoint security, and new technologies e.g., IoT. The research methodology applied is a review and case study analysing secondary data on cybersecurity. The research significance is the integration of knowledge from the United States (US), the European Union (EU), the United Kingdom (UK), and international standards and frameworks on cybersecurity that can be alighted to new Blockchain projects. The results show that cybersecurity standards are not designed in close cooperation between the two major western blocks - US and EU. In addition, while the US is still leading in this area, the security standards for cryptocurrencies, internet-of-things, and blockchain technologies have not evolved as fast as the technologies have. The key finding from this study is that although the crypto market has grown into a multi-trillion industry, the crypto market has also lost over 70% since its peak, causing significant financial loss for individuals and cooperation’s. Despite this significant impact to individuals and society, cybersecurity standards and financial governance regulations are still in their infancy.
Manju Dahiya, Naman Mishra, Chinki Nagar, Ruby Bhati
The Cyber Financial domain has changed a lot over the past few years but with the digitalization of the same, the various gaps in the field of security have risen. In terms of Finance, although there has been the implementation of technology 4.0 there is a lack of proper regulations, and any countermeasures are still absent to make the system secure and prevent any attack on data integrity. This research builds a bridge in the security aspects of cyberspace as well as provides a comprehensive framework which is based on the blockchain network to improve cybersecurity. The various issues with the increasing number of intrusive attacks, data breaches, and system failures have made it important to adopt proper measures to curb the spread of such attacks. To make cyberspace more secure and understand the different types of intrusion attacks an IDS is developed to make use of self-learning to continually grow and thus be more efficient. The proposed VCM framework also makes use of a blockchain network for decentralization as well as the use of smart contracts are also discussed which gives an idea as to the depth of the cybersecurity which can be maintained with the help of this. All the proposed framework is centred around the vulnerabilities of cyberspace which are being continuously exploited by malicious attackers and thus the VCM acts in a comprehensive way to maintain the security, privacy as well as robustness of the system by enforcing certain regulations.
The smart contract, a self-executing program on the blockchain, is key to programmable finance. However, the rise of smart contract use has also led to an increase in vulnerabilities that attract illegal activity from hackers. Traditional manual approaches for vulnerability detection, relying on domain experts, have limitations such as low automation and weak generalization. In this paper, we propose a deep learning approach that leverages domain-specific features and an attention mechanism to accurately detect vulnerabilities in smart contracts. Our approach reduces the reliance on manual input and enhances generalization by continuously learning code patterns of vulnerabilities, specifically detecting various types of vulnerabilities such as reentrancy, integer overflow, forced Ether injection, unchecked return value, denial of service, access control, short address attack, tx.origin, call stack overflow, timestamp dependency, random number dependency, and transaction order dependency vulnerabilities. In order to extract semantic information, we present a semantic distillation approach for detecting smart contract vulnerabilities. This approach involves using a syntax parser, Slither, to segment the code into smaller slices and word embedding to create a matrix for model training and prediction. Our experiments indicate that the BILSTM model is the best deep learning model for smart contract vulnerability detection task. We looked at how domain features and self-attentiveness mechanisms affected the ability to identify 12 different kinds of smart contract vulnerabilities. Our results show that by including domain features, we significantly increased the F1 values for 8 different types of vulnerabilities, with improvements ranging from 7.35% to 48.58%. The methods suggested in this study demonstrate a significant improvement in F1 scores ranging from 4.18% to 38.70% when compared to conventional detection tools like Oyente, Mythril, Osiris, Slither, Smartcheck, and Securify. This study provides developers with a more effective method of detecting smart contract vulnerabilities, assisting in the prevention of potential financial losses. This research provides developers with a more effective means of detecting smart contract vulnerabilities, thereby helping to prevent potential financial losses.
Abstract Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors. Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud. Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc. Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud. Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.
As cryptocurrency (crypto) has become more and more popular, so has crypto-related crime. There has been a lack of academic research on crypto-related crime, but it is becoming more prevalent in the last couple of years. Crypto-related crime became especially significant in the impact it had on victims and the awareness of these crimes in the media and the government in late 2019 and early 2020 as various criminal organizations and criminal opportunities opened up as cryptocurrency became mainstream. The common crimes related to cryptocurrency include fraud, theft, and money laundering. In 2021 estimates of crypto-related crime were estimated to be as high as $14 billion, which is a small fraction of a percent of the cryptocurrency transactions that were around $15.8 trillion in 2021. The purpose of the chapter is to provide a detailed account of the common crypto-related crimes and scams that have occurred and to evaluate the effectiveness of enforcement of these crimes.
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 SoK, we provide a comprehensive list of methods deployed by malicious actors on the network and illicit transaction mining methods. 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 cyber criminals.
Blockchain denial of service (BDoS) and selfish mining are the two most crucial attacks on blockchain technology. A classical DoS attack targets the computer network to limit, restrict, or stop accessing the system of authorized users which is ineffective against renowned cryptocurrencies like Bitcoin, Ethereum, etc. Unlike the conventional DoS, the BDoS affects the system's mechanism design to manipulate the incentive structure to discourage honest miners to participate in the mining process. In contrast, in a selfish mining attack, the adversary miner keeps its discovered block private to fork the chain intentionally that aiming to increase the incentive of the adversary miner. This paper proposed a technique to successfully avoid BDoS and selfish mining attacks. The existing infrastructure of blockchain technology does not need to be changed a lot to incorporate the proposed solution.
Fan Jiang, Kailin Chao, Jianmao Xiao, Qinghua Liu · 7 authors
As blockchain technology continues to advance, smart contracts, a core component, have increasingly garnered widespread attention. Nevertheless, security concerns associated with smart contracts have become more prominent. Although machine-learning techniques have demonstrated potential in the field of smart-contract security detection, there is still a lack of comprehensive review studies. To address this research gap, this paper innovatively presents a comprehensive investigation of smart-contract vulnerability detection based on machine learning. First, we elucidate common types of smart-contract vulnerabilities and the background of formalized vulnerability detection tools. Subsequently, we conduct an in-depth study and analysis of machine-learning techniques. Next, we collect, screen, and comparatively analyze existing machine-learning-based smart-contract vulnerability detection tools. Finally, we summarize the findings and offer feasible insights into this domain.
Over the past couple of years, smart contracts have been plagued by multifarious vulnerabilities, which have led to catastrophic financial losses. Their security issues, therefore, have drawn intense attention. As countermeasures, a family of tools has been developed to identify vulnerabilities in smart contracts at the source-code level. Unfortunately, only a small fraction of smart contracts is currently open-sourced. Another spectrum of work is presented to deal with pure bytecode, but most such efforts still suffer from relatively low performance due to the inherent difficulty in restoring abundant semantics in the source code from the bytecode.
Researcher and Assistant Professor Dr. & Scarborough Street, Southport, Gold Coast, Queensland, 4215, Australia, Bundit Anuyahong, Nipol Ek-udom
<strong>ABSTRACT: </strong>This study aimed to investigate the impact of cryptocurrency on global trade and commerce. The research objectives included examining the extent to which the adoption of cryptocurrency has disrupted traditional financial systems and affected cross-border transactions, as well as investigating the potential benefits and challenges of using cryptocurrency for global trade and commerce. A mixed-methods research design was used, incorporating both quantitative and qualitative data collection and analysis techniques. The study involved an extensive literature review, a survey of businesses involved in international trade, and interviews with key stakeholders. The results showed that cryptocurrency offers benefits such as reduced transaction costs, faster settlement times, and increased transparency in transactions. However, there are also challenges such as regulatory and legal hurdles, security concerns, and limited understanding of cryptocurrency. The study also highlights the factors that influence the adoption of cryptocurrency in international trade, including regulatory and legal frameworks, security concerns, awareness and understanding of cryptocurrency, transaction costs, and integration with existing systems. Finally, the attitudes and perceptions of businesses towards cryptocurrency are discussed, with the study showing that confidence in the reliability and security of cryptocurrency is a significant factor for adoption. Overall, the study provides insights into the potential opportunities and challenges presented by cryptocurrency in global trade and commerce, and the implications for policymakers, businesses, and investors.