Primavera De Filippi, Morshed Mannan, Wessel Reijers
ABSTRACT Emerging technologies pose many new challenges for regulation and governance on a global scale. With the advent of distributed communication networks like the Internet and decentralized ledger technologies like blockchain, new platforms emerged, disrupting existing power dynamics and bringing about new claims of sovereignty from the private sector. This special issue addresses a gap in the literature by focusing the discourse on the issue of trust and confidence in the digital realm. In particular, looking at the evolution of the web (from Web 1.0, to Web 2.0, and then Web 3), this article analyses how every iteration reflects a different way of dealing with the problem of trust online, resulting in a different regulation and governance landscape. Technology is often regarded as a new lever of regulation, attempting to resolve the problem of “trust” online, either through the introduction of a new trusted authority (Web 2.0) or through the introduction of technological guarantees that provide more assurance—or “confidence”—in the way interactions can be operationalized (Web 3). Yet, each of these technologies also introduce new risks and governance costs, ultimately shifting the problem of trust in a new direction rather than resolving it or removing the need for trust altogether. The main contribution of the articles in this special issue is providing a better understanding of the trust challenges faced and posed by emerging technologies and demonstrating how they affect institutional governance—in both theory and practice—with a view to help policymakers find appropriate answers to these challenges.
Distributed Ledger Technologies (DLTs) and smart contracts are revolutionizing industries by enabling transparent, decentralized, and automated transactions. However, the security of smart contracts remains a significant concern, as vulnerabilities can undermine the reliability of such systems and lead to substantial financial losses. Despite the critical importance of ensuring their integrity, there is a notable lack of automated frameworks to comprehensively assess smart contracts' security throughout their lifecycle, leaving them susceptible to various threats. This position paper proposes a framework to enhance smart contract security auditing, i.e., to efficiently and effectively support smart contract code analysis and testing and identify critical vulnerabilities. The framework encompasses several key components: identification of a target security profile, prioritization of potential vulnerabilities, systematic testing planning and execution, and a robust auditing and certification process. By establishing a structured approach to testing, we aim to enhance the security and reliability of smart contracts. In addition, we analyze the open challenges that must be addressed to build this framework effectively.
Cryptocurrencies and blockchain technology have revolutionized the financial sector, offering decentralized, secure, and efficient transaction mechanisms. However, these innovations have also introduced new challenges, particularly in the realm of financial crimes such as money laundering, illicit trade, and fraud. This paper explores the dual-use nature of cryptocurrencies, examining their potential for both financial innovation and criminal exploitation, with over $20 billion in illicit transactions recorded in 2023 (Chainalysis, 2023). By reviewing case studies, regulatory responses, and technological solutions, this paper provides a comprehensive analysis of the risks and opportunities presented by cryptocurrencies and blockchain technology. Current regulatory frameworks, such as the EU’s MiCA Regulation (2023) and FATF recommendations and guidelines, have significantly influenced cryptocurrency adoption by balancing innovation with risk mitigation. The paper concludes with actionable recommendations for enhancing regulatory frameworks, fostering international cooperation, leveraging AI and other technological advancements, and creating educational initiatives to mitigate financial crimes in the digital age.
Nasir Nadeem, Ahmad Ramli Saad, Zeeha Aslam, Zohaa Naveed
The purpose of this study is to assess this form of technocrimeand identify the misinformation gaps to restrict area suggestions, educational offer frameworks, and legislative proposals aimed at advancing the digital financial literacy of prospective young investors. The research aims to highlight how these scams affect multilateral financial inclusion, economic empowerment, and a reliable digital financial ecosystem. These schemes are targeted at university students who possess low financial literacy and are lured by the prospects of easy money, which endanger their lives in the long run. This descriptive research is based on an online survey conducted among students of Multan, using simple random sampling, collected through an online survey. The study will analyze the relationship between financial literacy and victimization in order to test the hypothesis that those with lower literacy are more susceptible. The research will also look into the disinformation marketing and recruitment strategies on social media and other Internet platforms regarding cryptocurrency. The study aids in accomplishing SDG 8: Decent Work and Economic Growth within the context of Pakistan’s digital economy. This research helps to understand the contribution of technocrime to the obstacles of financial inclusion and helps to develop a strongerdigital economic infrastructure proposal.
Blockchain technology is revolutionizing various industries through decentralized architecture and secure transaction mechanisms, yet its core application—smart contracts—faces increasingly sophisticated security threats. Recognizing the critical need for enhanced protection in this emerging domain, this paper introduces TPH-Fuzz, a two-phase hybrid fuzzing framework designed to overcome current limitations in vulnerability detection. TPH-Fuzz combines global exploration with local vulnerability targeting. It utilizes dynamic symbolic execution for semantics-aware path analysis and employs data-dependency-based state modeling to generate effective transaction sequences. These methods improve both path exploration and vulnerability detection precision significantly. Experiments on a coverage dataset of 9309 contracts demonstrate an 85% branch coverage on complex contracts, outperforming conventional methods; meanwhile, tests on a vulnerability dataset of 1086 labeled contracts show a detection precision of 89.24% across eight vulnerability categories. The promising results underscore the framework’s potential to transform security auditing practices in the blockchain industry, paving the way for more reliable smart contract development and deployment.
Advances in digital technology - particularly Web3’s pseudonymity and decentralized naming systems, combined with information flows’ anonymity, accessibility, and cross-border nature - enable terrorist organizations to recruit members and perpetrate discrete socially dangerous acts. Conventional reactive counterterrorism measures prove inadequate against rapid illicit content dissemination that leaves detectable digital traces. This study explores artificial intelligence’s (AI) counter-criminal potential on machine learning and predictive analytics for proactively identifying and preventing terrorist activity through behavioral indicators and digital footprints that facilitate a strategic shift to proactive security paradigms. The research develops a multimodal analytical framework integrating natural language processing, computer vision, audio analysis, and social network analysis, detailing the complete machine learning pipeline from data preprocessing to model deployment. It examines the “RED-Alert” system as practical implementation and proposes a novel “Threshold Adaptive Intervention” (PORA) module utilizing graph neural networks and time-series analysis for digital risk assessment. Machine learning excels at threat detection and digital evidence generating, necessitating reevaluation of internet service providers’ (ISP) liability - particularly collective digital inaction. A differentiated liability framework accounts for providers’ technical influence while treating AI-derived risk indicators as ancillary tools for establishing individual culpability. Machine learning and predictive analytics enable a strategic shift to proactive counterterrorism.
Open access
Terrorism, Counterterrorism, and Political Violence
Abstract Anti-money laundering has been an issue in our society from the beginning of time. It simply refers to certain regulations and laws set by the government to uncover illegal money, which is passed as legal income. Now, with the emergence of cryptocurrency, it ensures pseudonymity for users. Cryptocurrency is a type of currency that is not authorized by the government and does not exist physically but only on paper. This provides a better platform for criminals for their illicit transactions. New algorithms have been proposed to detect illicit transactions. Machine learning and deep learning algorithms give us hope in identifying these anomalies in transactions. We have selected the Elliptic Bitcoin Dataset. This data set is a graph data set generated from an anonymous blockchain. Each transaction is mapped to real entities with two categories: licit and illicit. Some of them are not labeled. We have run different algorithms for predicting illicit transactions like Logistic Regression, Long Short Term Memory, Support Vector Machine, Random Forest, and a variation of Graph Neural Networks, which is called Graph Convolution Network (GCN). GCN is of special interest in our case. Different evaluation parameters such as accuracy, ROC and F1 score are analyzed for different models. Our experimental results show that the proposed GCN model gives the accuracy $$98.5\%$$ , the AUC 0.9444 and the RMSE 0.1123, which concludes that our GCN is better than the existing models, in particular with the model proposed in Weber et al. (Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics, 2019. http://arxiv.org/abs/1908.02591 ).
Market sentiment refers to the overall feeling of investors and traders have about the state of the market or the price action of a particular asset. The descriptive study focused on the impact of market sentiment on cryptocurrency investment. Specifically, this study answered the following questions using the data collected in an online survey with 2014 respondents: (1) What is the current status of the market sentiment on cryptocurrency investment? (2) What are the common problems encountered by investors in the cryptocurrency market? (3) Is there a significant relationship between the current status of market sentiment on cryptocurrency investment and common problems encountered by investors? And; (4) What countermeasures can be proposed to the impact of market sentiment on cryptocurrency investment? Most of the respondents tended agreed about the current status of the market sentiment on cryptocurrency investment. Their responses tended to generally reflect their optimistic or "bullish" sentiment toward the market, that cryptocurrency holders are knowledgeable about the benefits of positive market outlook, and the market accurately predicted the volatility of cryptocurrency. The respondents were aware of the problems already reported in the literature, including cryptocurrency investment has become an avenue for illegal operations, the emergence of crypto scams, and the complexities of investing in cryptocurrencies. There was no significant correlation between market sentiment and problem encountered by investors. The respondents proposed some countermeasures to ameliorate some of the problems and challenges. The conclusions were confounded by Simpson's paradox. Further research is research to determine if the relationships between the current status of market sentiment on cryptocurrency investment vs. the common problems encountered by investors vary with respect to different mutually exclusive groups of investors.
Junhao Wu, Yixin Yang, Chengxiang Jin, Silu Mu · 8 authors
With the widespread adoption of Ethereum, financial frauds such as Ponzi schemes have become increasingly rampant in the blockchain ecosystem, posing significant threats to the security of account assets. Existing Ethereum fraud detection methods typically model account transactions as graphs, but this approach primarily focuses on binary transactional relationships between accounts, failing to adequately capture the complex multi-party interaction patterns inherent in Ethereum. To address this, we propose a hypergraph modeling method for the Ponzi scheme detection method in Ethereum, called HyperDet. Specifically, we treat transaction hashes as hyperedges that connect all the relevant accounts involved in a transaction. Additionally, we design a two-step hypergraph sampling strategy to significantly reduce computational complexity. Furthermore, we introduce a dual-channel detection module, including the hypergraph detection channel and the hyper-homo graph detection channel, to be compatible with existing detection methods. Experimental results show that, compared to traditional homogeneous graph-based methods, the hyper-homo graph detection channel achieves significant performance improvements, demonstrating the superiority of hypergraph in Ponzi scheme detection. This research offers innovations for modeling complex relationships in blockchain data.
Abstract Smart contracts and blockchain technology have revolutionized our transactions and interactions with digital systems, yet their vulnerabilities can lead to devastating consequences such as financial losses, data breaches, and compromised system integrity. Existing detection methods, including static analysis, dynamic analysis, and machine learning-based approaches, have their limitations, such as requiring large amounts of labeled data or being computationally expensive. To address these limitations, we propose a novel approach that leverages a One-Class Variational Autoencoder (VAE) with CodeBERT for data pre-processing to detect vulnerabilities in smart contracts. Our approach achieved a higher F1 score (88.93%) compared to the baselines evaluated, even when labeled data is limited. This paper contributes to the development of effective and efficient vulnerability detection methods, ultimately enhancing the security and reliability of smart contracts and blockchain-based systems. By demonstrating superior performance in imbalanced data scenarios, our method offers a practical solution for real-world applications in blockchain security.
Kyounggon Kim, Seok‐Hee Lee, Sundaresan Ramachandran, Ibrahim Alzahrani
Cybercriminals are employing sophisticated techniques to illegally obtain money from victims, with ransomware, that is the most notorious malware utilized for financial gain. This paper focuses on the Arab world, a prime target region for ransomware gangs. Due to rapid economic growth and digitalization in this region, cybercriminals are increasingly targeting it. However, there is a lack of research on ransomware crime syndication in the Arab region. Data on claimed ransomware victims from 2020 to 2023 was collected from the darknet. Analysis of ransomware gangs in this area revealed significant findings. Based on three years of data collection and analysis, 20 ransomware gangs primarily operating in the Arab region were identified in 2023. Three major ransomware gangs-LockBit, ALPHV/BlackCat, and CL0P-are predominantly targeting the Arab world, with the United Arab Emirates and Saudi Arabia being major targets, along with the manufacturing industry. In addition to identifying the ransomware gangs, the tactics, techniques, and procedures (TTP) used by them were also identified. There was 17 TTPs used by ransomware gangs. This study has also developed a platform to track ransomware gangs and cryptocurrency transactions. Bitcoin’s anonymity and popularity made it the most preferred cryptocurrency by ransomware gangs. This research lays the groundwork for further studies to understand the exact trends and data related to ransomware in the Arab world.
Stefano Ferretti, Gabriele D’Angelo, Vittorio Ghini, Marco B. Tomasone
This study investigates the detection of vulnerabilities in smart contracts using various transformer models and Large Language Model (LLM) systems. We evaluated BERT, CodeBERT, DistilBERT, and the Gemini model, employing techniques such as aggregation of chunks to enhance performance. The results indicate that simple transformers applied to source code generally perform worse than when applied to byte-code. However, the use of aggregation techniques on the source code significantly improved the model performance. We also evaluate the use of meta-classifiers for multimodal data, by stacking multiple transformers working on source code and byte-code. The Random Forest meta-classifier achieved the highest performance but exhibited significant overfitting. The Gemini model demonstrates limited performance, highlighting the necessity of proper training for LLM systems.
As with the increasing usage of blockchain especially in decentralized systems, the need for solid security measures increases to stop vulnerabilities in smart contracts. Our method is an ML-based solution to automate the detection of smart contract vulnerabilities and improves the blockchain security. In this paper, we propose a comprehensive vulnerability detection algorithm that employs a hybrid detection mechanism that analyzes transaction patterns and uses ML models to detect suspicious activities on the fly. The algorithm detects as well as flags smart contract vulnerabilities and malicious transactions by analyzing transaction amounts, user behavior, and frequency. The developed framework not only lessens manual effort but also automates validation so as to improve mitigation process in case of unforeseen risks. This essentially shows the capability of using ML-driven analysis to secure the blockchain environment as an adaptable approach for the detection of smart contract vulnerabilities.
The advent of decentralised applications across a range of sectors has led to a growing emphasis on the research and development of methods to identify vulnerabilities in smart contracts for decentralised applications. However, current detection techniques have been found to have limitations in terms of accuracy and the number of false alarms they generate. In order to address the aforementioned issues, this paper puts forth a modular vulnerability detection model, designated as BAMC. The method initially utilises the word2vec model to derive the word vector representation of the smart contract, subsequently extracting the word order information through a bidirectional long short-term memory network. Subsequently, the attention mechanism and max-pooling operation are employed to process the word order information, thereby obtaining fine-grained features and key features. Ultimately, explicit bounded-degree feature interactions are achieved through the combination of deep and cross networks, thus enabling the detection of reentrancy vulnerabilities and timestamp vulnerabilities. The experimental results demonstrate that the proposed method exhibits superior performance in comparison to existing techniques, with significantly higher values for various indexes. Notably, the reentrancy vulnerability and the - of timestamp vulnerability reach 86.14 and 91.43 , respectively.
Oshoke Samson Igonor, Muhammad Bilal Amin, Saurabh K. Garg†
Blockchain technology has risen in recent years from its initial application in finance to gain prominence across diverse sectors, including digital forensics. The possible application of blockchain technology to digital forensics is now becoming increasingly explored with many researchers now looking into the unique inherent properties that blockchain possesses to address the inherent challenges in this sector such as evidence tampering, the lack of transparency, and inadmissibility in court. Despite the increasing interest in integrating blockchain technology into the field of digital forensics and its domains, no systematic literature review currently exists to provide a holistic perspective on this integration. It is a challenge to find a comprehensive resource that examines how blockchain is being applied to enhance the digital forensics process. This paper provides a systematic literature review to explore the application of blockchain technology in digital forensics, focusing on its potential to address these challenges and enhance forensic methodologies. Through a rigorous review process, this paper examines selected studies to identify diverse frameworks, methodologies, and blockchain-driven enhancements applied to digital forensic investigations. The discussion highlights how blockchain properties such as immutability, transparency, and automation have been leveraged to improve evidence management and forensic workflows. Furthermore, this paper explores the common applications of blockchain-based forensic solutions across various domains and phases while addressing the associated limitations and challenges. Open issues and future research directions, including unexplored domains and operational gaps, are also discussed. This study provides valuable insights for researchers, investigators, and policymakers by offering a comprehensive overview of the state of the art in blockchain-based digital forensics, summarizing key contributions and limitations, and identifying pathways for advancing the field.
Current crime reporting systems suffer from data distortion, lack of transparency, and privacy vulnerabilities, undermining their credibility. This paper proposes a blockchainbased DApp for secure and transparent crime reporting, integrating advanced encryption, zero-knowledge proofs, instant notifications, and a role-based access mechanism to ensure integrity, anonymity, and clarity. Rigorous testing will demonstrate its functionality, reliability, and user satisfaction, offering improved security, privacy, and transparency over traditional methods.
The proliferation of cryptocurrency throughout society has led to widespread usage within criminal offending. Despite this, limited research has investigated fear of cryptocurrency-based victimization, or the role that financial or cryptocurrency literacy play in influencing such fears. This study examines the influence in which financial and cryptocurrency literacy plays in both personal and altruistic fear of financial and cryptoeconomic crimes. Using a sample of college undergraduates (n = 433), and employing a validated scale of cryptocurrency literacy, results indicate that cryptocurrency literacy is not significantly associated with all modalities of fear of crime investigated, with the exception of altruistic fear of non-cryptocurrency financial crimes. Conversely, general financial literacy was negatively associated with personal fear of both financial crimes and non- cryptoeconomic financial crimes. Findings are discussed in light of research and policy implication as well as limitations.
Blockchain technology consists of distributed ledgers or database systems, regarded as immutable, secure, and innovative, characterized by unsupervised internal maintenance with a special security protocol used to prevent inference from malicious or third parties. The widespread use of this technology has led to deep research into the problems posed by this technology, which can be summarized in terms of computational cost and latency time. The crime detection process in video surveillance has made great progress with the use of technologies such as the Internet of Things and blockchain technologies. However, to reach high levels of security in the physical crime detection process in which data are sent to servers via a computer network, there must be a high degree of security for Internet of Things systems related to the crime detection process. There has been a significant increase in the number of problems associated with crime detection in video surveillance systems, including the modification of surveillance data during transfer to and from servers. For this reason, establishing a reliable and secure system for transferring video surveillance data to servers has become a high priority. This paper presents a lightweight security system to protect data generated in the crime detection process, both from video surveillance cameras and the servers that store these data. The challenges related to IoT-based video surveillance cameras and monitoring and control centers have been considered, turning the system primarily into a decentralized system. In this paper, a lightweight blockchain system based on a proof of secret share consensus algorithm technology is proposed, along with the encryption of surveillance data via modified Okamoto–Uchiyama homomorphic encryption technology. The proposed system is evaluated via standard blockchain and security evaluation metrics, demonstrating efficient utilization of computational costs and realization of security, with a high scalability rate. The VGG16 deep learning model is employed in the proposed system to detect and classify criminal activities in surveillance videos. Owing to its ability to identify patterns and anomalies, the model achieved an accuracy of 94%, demonstrating a high level of performance in crime detection and prevention. Overall, the use of VGG16 provides an efficient and reliable approach for improving the security of public spaces and reducing the risk of criminal activity.
Bishwo Prakash Pokharel, Naresh Kshetri, Suresh Raj Sharma, S. Paudel
The COVID-19 pandemic exposed critical vulnerabilities in global healthcare systems, particularly in data security and interoperability. This paper introduces the blockHealthSecure Framework, which integrates blockchain technology with advanced cybersecurity measures to address these weaknesses and build resilient post-pandemic healthcare systems. Blockchain’s decentralized and immutable architecture enhances the accuracy, transparency, and protection of electronic medical records (EMRs) and sensitive healthcare data. Additionally, it facilitates seamless and secure data sharing among healthcare providers, addressing long-standing interoperability challenges. This study explores the challenges and benefits of blockchain integration in healthcare, with a focus on regulatory and ethical considerations such as HIPAA and GDPR compliance. Key contributions include detailed case studies and examples that demonstrate blockchain’s ability to mitigate risks like ransomware, insider threats, and data breaches. This framework’s design leverages smart contracts, cryptographic hashing, and zero-trust architecture to ensure secure data management and proactive threat mitigation. The findings emphasize the framework’s potential to enhance data security, improve system adaptability, and support regulatory compliance in the face of evolving healthcare challenges. By bridging existing gaps in healthcare cybersecurity, the blockHealthSecure Framework offers a scalable, future-proof solution for safeguarding health outcomes and preparing for global health crises.
Blockchain innovation, best embodied by Ethereum, has revolutionized online transactions by making them more transparent and secure. However, the demand for more sophisticated fraudulent schemes increases with wider adoption, calling for more sophisticated fraud detection methods. Therefore, this paper contributes to the area of blockchain security by providing insights to regulators and stakeholders in Ethereum through an analysis of the Machine Learning (ML) models. We compare traditional approaches like logistic regression and decision trees with more advanced techniques like neural networks and ensemble methods. The performance of the model is measured using accuracy, precision, recall, and the ROC curve. The best accuracy of 0.98 is achieved by the optimized XGBoost framework.