ABSTRACT The proliferation of phishing scam tokens on the Ethereum blockchain, including honeypot, rug pull, and impersonation schemes, poses a grave threat to financial security. Although earlier studies have documented detection accuracies that exceed 95%, they frequently depend on random train‐test partitions. These partitions frequently overestimate real‐world performance by disregarding the temporal progression of phishing behaviors. This study addresses the methodological gap by employing a temporally validated evaluation. A labeled dataset comprising 5408 Ethereum token contracts was constructed. This dataset was verified through a two‐stage process that integrated cyber threat intelligence and on‐chain evidence. A total of 16 discriminative features were extracted, reflecting transaction volume, network structure, and temporal behavior. In lieu of employing random partitioning, temporal validation (70% training, 15% validation, and 15% testing) was adopted to assess generalizability to emerging threats. Six machine learning models (LightGBM, XGBoost, Random Forest, Gradient Boosting, Decision Tree, and MLP) were tuned via GridSearchCV. LightGBM demonstrated optimal performance, attaining 85.59% accuracy, 81.63% F1‐score, and 92.02% AUC on temporally held‐out data. The feature ablation process yielded the identification of transaction volume as the most discriminative factor, with a corresponding increase in performance of 13.09 points on the performance scale. Conversely, temporal features exhibited a marginal decline in performance, with a decrease of 0.87 points. Temporal validation resulted in a 3.95‐point‐percentage decrease compared to random splitting, thereby exposing the optimistic bias present in prior studies. Despite the fact that the resulting F1‐score of 81.63% falls short of the 85% threshold stipulated in the literature, it is indicative of a realistic deployment expectation. This work underscores the importance of temporal validation for reliable fraud detection research.
This chapter explores the extent of the complex relationship between cryptocurrency and unexplained wealth, emphasizing the dual nature of these digital assets as both tools for legitimate wealth accumulation and facilitators of financial crime. Despite their potential for investment and trading, cryptocurrencies have been implicated in significant criminal activities, with estimates indicating that nearly 46% of Bitcoin transactions involve illicit behaviour. This analysis delves into the mechanisms by which cryptocurrencies allow for wealth accumulation – ranging from investing in volatile markets to participating in initial coin offerings – while examining the inherent risks of anonymity, decentralisation, and minimal regulatory oversight that attract criminal actors. Furthermore, the paper discusses how these features enable market manipulation and facilitate illegal trade on dark web platforms. Additionally, this chapter discusses how cryptocurrencies enable wealth accumulation and offer mechanisms for obfuscating wealth, mainly through privacy coins like Monero and Zcash. These coins incorporate advanced encryption techniques, such as ring signatures, stealth addresses, and zero-knowledge proofs, which complicate efforts by law enforcement to trace transactions and identify users. Ring signatures obscure the identity of transaction signers, while stealth addresses disconnect the sender and receiver, ensuring anonymity. Zero-knowledge proofs provide verification without revealing sensitive information, enhancing confidentiality further. Finally, tumbling and mixing services aggregate multiple users’ transactions, obscuring the financial trail and making asset recovery exceedingly difficult.
The decentralized and anonymous nature of Ethereum makes it a prime target for phishing scams. These scams account for nearly 50% of all blockchain-related fraud, thereby causing a substantial financial loss and eroding user trust. Unlike conventional phishing, Ethereum phishing users exploit user anonymity, lack of awareness, and market-driven dynamics to deceive normal users. Despite of a plethora of research in this direction, there is a lack of a rigorous and comprehensive survey which can fortify an insightful comparison of the existing works and provide a concrete future research guidance. To this end, this paper presents a systematic review of 90 studies published between 2020 and 2024, offering the following novel contributions, (1) Structured Taxonomy: We introduce a structured three-fold taxonomy that classifies existing methods into feature engineering-based, representation learning-based, and fusion-based frameworks. (2) Theoretical Analysis: Through theoretical analysis, we evaluate these approaches against the critical research challenges, such as rapid network dynamism, data leakage, and network sparsity and provide a comparative mapping of novel techniques adopted across the studies. (3) Empirical Evaluation: We conduct an extensive empirical evaluation of 14 representative models over multiple public datasets to assess their robustness under varying data conditions. The findings indicate that while feature-based models are more interpretable, they struggle with temporal adaptability; representation learning approaches, particularly GNN-based models, capture complex behavioral patterns but are computationally demanding and less explainable. Fusion methods demonstrate the most balanced trade-off between accuracy, scalability, and interpretability. (4) Future Research Guidance: Finally, we identify still persisting issues such as network sparsity, behavioral volatility, and scalability, and outline future research directions emphasizing temporal graph reasoning, self-supervised fusion, and explainable AI for developing transparent and deployable phishing detection frameworks on Ethereum.
Non-fungible tokens (NFTs) serve as a representative form of digital asset ownership and have attracted numerous investors, creators, and tech enthusiasts in recent years. However, related fraud activities, especially phishing scams, have caused significant property losses. There are many graph analysis methods to detect malicious scam incidents, but no research on the transaction patterns of the NFT scams. Therefore, to fill this gap, we are the first to systematically explore NFT phishing frauds through graph analysis, aiming to comprehensively investigate the characteristics and patterns of NFT phishing frauds on the transaction graph. During the research process, we collect transaction records, log data, and security reports related to NFT phishing incidents published on multiple platforms. After collecting, sanitizing, and unifying the data, we construct a transaction graph and analyze the distribution, transaction features, and interaction patterns of NFT phishing scams. We find that normal transactions on the blockchain accounted for 96.71% of all transactions. Although phishing-related accounts accounted for only 0.94% of the total accounts, they appeared in 8.36% of the transaction scenarios, and their interaction probability with normal accounts is significantly higher in large-scale transaction networks. Moreover, NFT phishing scammers often carry out fraud in a collective manner, targeting specific accounts, tend to interact with victims through multiple token standards, have shorter transaction cycles than normal transactions, and involve more multi-party transactions. This study reveals the core behavioral features of NFT phishing scams, providing important references for the detection and prevention of NFT phishing scams in the future.
This study proposes a gamification-based educational model that integrates blockchain concepts and reinforcement learning (RL) principles for elementary students.While blockchain education is often abstract and unsuitable for younger learners, the proposed card game-based approach allows students to experience hash functions, consensus algorithms, and distributed ledgers through interactive activities.Instructional design followed the Dick and Carey model, and the MDA framework was applied to align game mechanics with cognitive, emotional, and social objectives.RL mechanisms such as exploration-exploitation balance and reward shaping were embedded to sustain engagement and motivation.The model's effectiveness was evaluated through expert review involving four technology specialists and nine elementary school teachers.Results showed consistently positive ratings across five metrics (Innovation, Effectiveness, Applicability, Motivation, Efficiency), with averages above 3.8 on a 5-point scale.Particularly high scores were recorded for Innovation (M=4.14) and Efficiency (M=4.09).These findings indicate that the model is both novel and practical, offering a promising approach to making abstract technical concepts accessible at the elementary level.
Permissionless blockchains have evolved beyond cryptocurrency into foundations for Web3 applications, decentralized finance (DeFi), and digital asset ownership, yet this rapid expansion has intensified privacy vulnerabilities. This study provides a comprehensive review of recent trends, emerging privacy threats, and mitigation strategies in permissionless blockchain ecosystems. We examine six developments reshaping the landscape: meme coin proliferation on high-throughput networks, real-world asset tokenization linking on-chain activity to regulated identities, perpetual derivatives exposing trading strategies, institutional adoption concentrating holdings under regulatory oversight, prediction markets creating permanent records of beliefs, and blockchain–AI integration enabling both privacy-preserving analytics and advanced deanonymization. Through this work and forensic analysis of documented incidents, we analyze seven critical privacy threats grounded in verifiable 2024–2025 transaction data: dust attacks, private key management failures, transaction linking, remote procedure call exposure, maximal extractable value extraction, signature hijacking, and smart contract vulnerabilities. Blockchain exploits reached $2.36 billion in 2024 and $2.47 billion in the first half of 2025, with over 80% attributed to compromised private keys and signature vulnerabilities. We evaluate privacy-enhancing technologies, including zero-knowledge proofs, ring signatures, and stealth addresses, identifying the gap between academic proposals and production deployment. We further propose a Secure Development Lifecycle framework incorporating measurable security controls validated against incident data. This work bridges the disconnect between privacy research and industrial practice by synthesizing current trends, providing insights, documenting real-world threats with forensic evidence, and providing actionable insights for both researchers advancing privacy-preserving techniques and developers building secure blockchain applications.
Cryptocurrency exchanges are integral to the digital asset economy; however, their rapid growth has been accompanied by recurrent high-impact cyberattacks that erode trust and inflict substantial losses. Guided by the PRISMA-ScR framework, this review systematically screened peer-reviewed and industry sources to construct a validated dataset of 220 major incidents (2009–2024) across centralized (CEX) and decentralized (DEX) exchanges. We classify attack vectors, analyze repeated high-impact patterns, and identify systemic vulnerabilities spanning cryptographic mechanisms and exchange infrastructure. Across CEX platforms, four of ten identified attack types accounted for 62 of the 80 incidents and approximately $1.764 billion in losses (42.1% of the $4.191 billion CEX total). Across DEX platforms, five of eighteen attack types were responsible for 120 of 140 incidents, totaling $3.755 billion (87.3% of the $4.303 billion DEX total). The overall losses sum to $8.494 billion across 220 incidents (80 CEX; 140 DEX). Repeated vectors comprised 182/220 incidents and $5.519 billion (65.0%) of losses, dominated by wallet/key compromise (78 incidents; $2.394 billion) and DEX system/server/protocol exploits (56 incidents; $1.939 billion); these two classes account for 134/182 repeated incidents (79.1%) and $4.333 billion (78.5%) of repeated losses. We examine the susceptibility of cryptographic defenses to emerging quantum adversaries and assess the exchange readiness for post-quantum threats. This study is the first to systematically compile and quantitatively analyze cybercrime incidents affecting both centralized and decentralized cryptocurrency exchanges in a unified dataset, enabling unprecedented comparability of systemic risks with actionable insights for cybersecurity researchers, regulators, and exchange operators seeking quantum-safe infrastructure evolution.
How do the emerging Web 3.0 technologies affect the survival of non-state armed groups (NSAGs) in their violent struggles vis-à-vis state entities? While techno-optimists argue that Web 3.0 can democratize the internet and curb monopolistic practices, its decentralized features, such as enhanced privacy, data ownership, and personalization, also present significant security challenges. These technologies can be weaponized by NSAGs to promote their efficiency and resilience. Borrowing insights from social movement theory, we construct a theoretical framework to explain how Web 3.0 applications affect the dynamics of NSAGs by impacting their organizational modes and strategies. It is argued that blockchain-based platforms, metaverse projects, and other Web 3.0 technologies promote the efficiency of the recruitment, training, financing, purchasing, and communication processes of NSAGs, increasing their capacities as social organizations, and thereby render these groups more resilient to collapse. We illustrate and corroborate our theoretical claims by examining the cases of how NSAGs such as the Islamic State utilize decentralized crypto exchanges and the Dark Web in their operations.
Open access
Terrorism, Counterterrorism, and Political Violence
Regardless of one’s opinion, Bitcoin’s presence in the global economy is growing. However, Bitcoin’s emerging role and its implications are greatly under-researched, particularly in the context of government interest in Bitcoin. Nonetheless, increased private investment in Bitcoin and increased government interest in formally incorporating Bitcoin into existing economic systems, suggest a new development within the global economy that must be investigated. Through qualitative text analysis of pro-Bitcoin narratives presented in digital media platforms and official government policies and public statements, this thesis explores how private and government interest in Bitcoin is explained and framed within these contexts. This study finds that there are many important nuances within pro-Bitcoin narratives in the context of private interest that challenge and expand contemporary thinking. It presents new insights into government interest in Bitcoin, particularly concerning its intended role and future, suggesting it will have a presence in efforts beyond finance. Finally, this thesis suggests that despite converging attitudes in private and governmental pro-Bitcoin narratives, diverging attitudes reflect curious implications concerning distrust and dissatisfaction in government efforts. Ultimately, this study reflects that Bitcoin is a dynamic and non-traditional development that requires continuous research to better understand its present and future role in global systems.
Abstract: Identity theft has emerged as a psychologically consequential form of cybercrime enabled by the proliferation of digital platforms, the expansion of datafication, and the collapse of traditional criminal–victim proximity. As personal identity becomes increasingly externalized through financial accounts, medical records, biometric templates, and algorithmically curated social profiles, offenders exploit cognitive biases, disclosure fatigue, and habituated oversharing to acquire and weaponize personal information. Criminal psychology research demonstrates that social engineering, authority mimicry, and emotional urgency manipulate victims into bypassing rational scrutiny, while cyberpsychology highlights the affective attachment individuals form with their digital representations. Unlike conventional theft, in which tangible objects are removed, identity theft appropriates informational components of the self, enabling prolonged impersonation, reputational distortion, and chronic anxiety that cannot be readily restored. Geographic detachment, encrypted communication channels, and anonymizing technologies reduce offenders’ perceived accountability, encouraged moral disengagement and facilitating mass victimization at minimal personal risk. Victims, confronted with unauthorized transactions or corrupted medical histories, report hypervigilance, loss of digital agency, and destabilization of narrative coherence. Emerging technologies, including Internet of Things devices, deepfake media, decentralized finance, and eventually quantum computing, further expand the attack surface and amplify criminogenic opportunity structures. Meanwhile, jurisdictional fragmentation complicates forensic attribution and legal recourse. Collectively, these developments reveal that traditional, place-based models of personal security are insufficient in networked environments. Safeguarding informational sovereignty requires interdisciplinary approaches that integrate behavioral criminology, cognitive vulnerability assessment, cyberpsychological resilience, and international policy coordination. Understanding identity theft as an ontological, relational, and psychologically persistent violation offers critical insight for prevention, victim support, and regulatory design in the digital epoch. Keywords: Identity Theft; Cyberpsychology; Criminal Psychology; Datafication; Digital Proximity Collapse; Social Engineering; Informational Sovereignty; Biometric Fraud; Cognitive Vulnerability; Cybercrime Scalability
Open access
2 source records
Cybercrime and Law Enforcement Studies
Crime Patterns and Interventions
Psychopathy, Forensic Psychiatry, Sexual Offending
Our study, set against the backdrop of the ongoing Russia-Ukraine war, Hamas-Israel conflict and fintech advancements, investigates the implications of Bitcoin (BTC), Ethereum (ETH), and Tether (USDT)'s active addresses on BTC prices. In doing this, we employ a combination of traditional time series VAR model and machine learning BPNN model during the geopolitical conflict period. Our findings indicate that BTC and ETH prices often move in tandem ahead of geopolitical conflicts. Investors aiming to boost their income choose to buy lower-priced ETH, leading to a rise in the number of active ETH addresses, positively correlated with BTC's price. However, during geopolitical conflicts, this relationship shifts. The number of active USDT addresses is a significant factor influencing BTC price. Consequently, when confronted with a steep decline in BTC prices, investors tend to convert BTC into the USDT stablecoin to avert losses.
The crisis of research integrity triggered by academic misconduct, such as scientific fraud and paper retractions, has emerged as a critical issue demanding urgent resolution within the academic community. Blockchain (BC), with its core features of distributed ledger, peer-to-peer transmission, consensus mechanisms, timestamps, and smart contracts, offers novel technical solutions for research institutions seeking efficient models of research credit supervision. By incorporating the psychological factors of risk perception among decision-makers and the dynamic evolution of behavioral decision-making, and drawing on prospect theory, this study has constructed an evolutionary game model involving researchers, scientific research institutions, and governmental entities to examine BC-enabled research credit supervision. This model analyzes the key determinants influencing scientific research institutions’ adoption of blockchain regulation (BC regulation), elucidates the behavioral characteristics and boundary conditions of research integrity among researchers under this new regulatory paradigm, and reveals the dynamic evolutionary trajectory of collaborative supervision between governments and scientific research institutions. The findings indicate the following: (1) Compared to traditional regulation, the BC regulation demonstrates superior regulatory effectiveness at equivalent levels of researcher integrity and misconduct costs, as well as under identical settings for reputational loss and penalties. (2) In addition to cost considerations and government subsidies, factors such as loss aversion coefficient, risk preference coefficient, and privacy breach losses are critical in influencing research institutions’ decisions to implement BC regulation. (3) The evolution of blockchain-empowered regulatory models encompasses three distinct evolutionary patterns. This study provides a theoretical foundation and a simulation case to optimize regulatory strategy formulation and resource allocation, thereby enhancing the effectiveness of research credit supervision.
S. N. Jain, Rupprashik A. Khare, Samyak Lahire, Chaitali Patil
There are more than over 23,000 cryptocurrencies” in existence which represent one of the biggest unregulated markets across the globe. Currently, each cryptocurrency utilises distinct public-private key and public address formats. This diversity complicates the investigator's task of tracing and verifying a suspect's involvement in cryptocurrency-related crimes. Moreover, law enforcement agencies such as the police frequently seize digital devices, generating digital disk images for investigation which is a time consuming process. Thus, the investigation of cryptocurrencies has emerged as a major challenge. for law enforcement agencies across the world. Our proposed solution “CryptoKalp” aims to combat cybercrimes involving cryptocurrencies. The essence of our proposed tool is encapsulated in its name: “CryptoKalp.” The name is a fusion of two significant terms - “crypto,” signifying cryptocurrency, and “kalp,” a Sanskrit word representing investigation [1]. Our proposed tool makes the investigation process quicker by taking digital images, strings, various formats of files and folders as input and utilising a comprehensive database of regular expressions covering various cryptocurrency public-private key and address formats. Currently, our tool successfully identifies Bitcoin public-private keys and addresses from text files, with future plans outlined expanding its capabilities for Ether, Tether, Monero, Dash and Dogecoin. Investigating officers can access the system's user-friendly interface and the database containing login credentials, cryptocurrency formats, and history of searched strings, which enables officers to track a specific criminal associated with a series of crypto transactions. Police can confirm whether a suspect is involved in cryptocurrency transactions and take necessary actions. Our solution will aid law enforcement authorities in combating cybercrime.
The prosperity of Ethereum gives rise to a new type of transaction-based phishing scam. Specifically, users are tempted to visit phishing websites and sign phishing transactions that allow scammers to withdraw their tokens. Meanwhile, to accelerate the deployment of phishing websites, scammers have introduced a business model, Drainer-as-a-Service (DaaS). In this model, drainer operators focus on crafting specialized phishing toolkits, named ''wallet drainers'', while drainer affiliates handle the deployment and promotion of phishing websites. After stealing victims' tokens, they will distribute profits. In this paper, we present the first systematic study of DaaS on Ethereum. To begin with, we propose a snowball sampling approach to build the first large-scale DaaS dataset, including 1,910 profit sharing contracts, 56 operator accounts, 6,087 affiliate accounts, and 87,077 profit-sharing transactions. Then, we analyze the scale of DaaS from the perspectives of victims, operators, and affiliates, and perform clustering analysis to uncover dominant DaaS families. Finally, we reported DaaS accounts in the dataset and 32,819 phishing websites deployed with DaaS toolkits to the community. Our work aims to serve as a guide for Ethereum service providers to enhance user protection against DaaS.
Jamila Tileubaevna Arzieva, Ali Tileubaevich Arziev, Nawrızbay Baxtiyar ul Seytniyazov, Ilham Kongratbay ulı Tlemisov
THE ROLE OF ZERO-KNOWLEDGE PROOFS IN ENHANCING CRYPTOGRAPHIC PROTOCOLS // Universum: технические науки : электрон. научн. журн. Arzieva J.T. [и др.]. 2025. 10(139). URL: https://7universum.com/ru/tech/archive/item/21052
The convergence of blockchain, artificial intelligence (AI), and cloud computing is catalyzing a paradigm shift in developing secure, intelligent, and scalable digital infrastructures. This triad of technologies is increasingly utilized to improve performance, transparency, and trust in engineering-driven and socio-technical environments. This study systematically reviews the evolution, integration strategies, and applications of blockchain, AI, and cloud computing in digital ecosystems. The analysis is based on 108 peer-reviewed studies spanning the years 2012 to 2025. A comprehensive literature analysis was conducted to identify trends, synergies, and sector-specific implementations of these systems. The review explores how their integration supports real-world engineering and operational use cases. Blockchain contributes to decentralized architectures, secure data exchange, and identity verification. AI supports adaptive behavior, autonomous decision-making, and predictive analytics. Cloud computing offers the scalable infrastructure necessary for deployment. Key challenges addressed include interoperability, latency, security trade-offs, and resource allocation. Use cases in digital finance, supply chain management, and industrial automation demonstrate the effectiveness of this integration in building resilient, ethically aligned, and high-performance infrastructures. The findings offer valuable insights and technical considerations for engineers and architects seeking to design next-generation cyber-physical systems that are secure, intelligent, and socially responsive. Clinical Trial Number Not applicable.
Luis de‐Marcos, Adrián Domínguez‐Díaz, Javier Junquera-Sánchez, Carlos Cilleruelo · 5 authors
The Dark Web, a hidden segment of the internet, has become a hub for illicit activities, facilitated by various forms of digital identification (IDs) such as email addresses, Telegram accounts, and cryptocurrency wallets. This study conducts a comprehensive analysis of the Dark Web’s identification and communication patterns, focusing on the roles of different ID types and their associated activities. Using a dataset of Dark Web documents, we construct and analyze a bipartite network to model the relationships between IDs and web documents, employing graph–theoretical metrics such as degree centrality, closeness centrality, betweenness centrality, and k-core decomposition, while analyzing subnetworks formed by ID type. Our findings reveal that Telegram forms the backbone of the network, serving as the primary communication tool for hacking-related activities, particularly within Russian-speaking communities. In contrast, email plays a more decentralized role, facilitating finance–crypto and other activities but with a high level of fragmentation and English as the predominant language. XMR (Monero) wallets emerge as a key component in financial transactions, forming a cohesive subnetwork focused on cryptocurrency-related activities. The analysis also highlights the modular and hierarchical nature of the Dark Web, with distinct clusters for hacking, finance–crypto, and drugs–narcotics, often operating independently but with some cross-topic interactions. This study provides a foundation for understanding the Dark Web’s structure and dynamics, offering insights that can inform strategies for monitoring and mitigating its risks.
Shikah J. Alsunaidi, Hamoud Aljamaan, Mohammad Hammoudeh
Smart Contracts (SCs), self-executing programs on blockchain platforms, are transforming industries such as banking, healthcare, and supply chains through automated, trustless transactions. However, their inherent vulnerabilities have led to severe financial and operational losses, with large-scale exploits causing substantial economic damage. Machine Learning (ML) has emerged as a promising approach for SC vulnerability detection, yet its effectiveness, adaptability, and generalizability remain insufficiently explored. This article comprehensively classifies current Ethereum SC vulnerabilities and attacks. It also surveys 108 ML-based detection methods, covering both traditional models and a structured taxonomy of advanced approaches such as GNN-based, LLM-based, contrastive learning, ensemble, hybrid, meta-learning, and transfer learning techniques. The strengths, limitations, and practical challenges of these methods are systematically analyzed, with particular attention to factors such as detection stages, classification problems, dataset characteristics, feature engineering, performance evaluation, generalizability, detection capability, model aging, and ethical and privacy implications. Additionally, existing datasets on SC vulnerabilities are reviewed and consolidated. By integrating these insights, this work provides actionable guidelines and a foundation for building secure, resilient, and trustworthy SC ecosystems.
Phishing attacks in Web3 ecosystems are increasingly sophisticated, exploiting deceptive contract logic, malicious frontend scripts, and token approval patterns. We present DeepTx, a real-time transaction analysis system that detects such threats before user confirmation. DeepTx simulates pending transactions, extracts behavior, context, and UI features, and uses multiple large language models (LLMs) to reason about transaction intent. A consensus mechanism with self-reflection ensures robust and explainable decisions. Evaluated on our phishing dataset, DeepTx achieves high precision and recall (demo video: https://youtu.be/4OfK9KCEXUM).
I‐Fang Su, Shun-Ming Wang, Yu-Chi Chung, Yi-Hsien Tsai
Abstract In this research, we introduce an advanced approach for the detection of smart contract vulnerabilities leveraging Large Language Models (LLMs). Smart contracts are pivotal in the ecosystem of decentralized finance (DeFi), functioning as automated protocols for data management and transaction execution. The foundation of numerous blockchain-based applications lies in smart contract technology. Nevertheless, these contracts’ code vulnerabilities can become targets for malicious exploitation, leading to substantial financial damages, exemplified by the 2016 Dao smart contract incident which incurred a loss of 55 million USD. In response to such challenges, detection mechanisms for smart contract vulnerabilities have been devised, drawing upon conventional static analysis, fuzzy testing, and machine learning methodologies. Owing to the swift progression of LLMs, such as GPT, a broad spectrum of entities has adopted these models for routine operational management. By recognizing LLMs’ inherent capability to comprehend programming code, we investigate their aptitude for identifying smart contract vulnerabilities. We have integrated prompt engineering techniques, including the Chain of Thought (CoT), Plan-and-Solve, and few-shot learning, to augment the LLMs’ vulnerability detection efficacy. Furthermore, a sequence of empirical studies has been orchestrated to validate the effectiveness of our proposed prompt engineering strategies against diverse smart contract vulnerabilities.
Abstract This study explores the intersection between cryptocurrencies and criminal organizations through a systematic literature review. By examining peer-reviewed academic publications, the research identifies the mechanisms by which criminal groups leverage digital currencies for illicit activities such as money laundering, fraud, and extortion. A structured PRISMA-based methodology was adopted, employing well-defined inclusion, and exclusion criteria to ensure transparency and replicability. The study reveals critical trends in the misuse of cryptocurrencies, the technological challenges faced by regulators, and the limitations of current enforcement frameworks. The originality of this research lies in synthesizing existing academic insights while identifying key gaps in the literature. The findings highlight the urgent need for tailored regulatory responses and greater cross-border cooperation, contributing to both academic understanding and policymaking. The practical implications include recommendations for policy adaptation and future research pathways to mitigate cryptocurrency-enabled criminal behavior.