Decentralized Finance (DeFi) is a recent advancement of the cryptocurrency ecosystem, giving plenty of opportunities for financial inclusion, innovation, and growth domains by providing services such as lending, borrowing, and trading without traditional intermediaries. However, inadequate regulatory oversight and technological vulnerabilities raise pressing concerns around market manipulation, fraud, and regulatory compliance, exposing a clear research gap in effective DeFi risk management. This paper addresses this gap by proposing a utility-based framework to evaluate six leading DeFi tracking platforms—Chainalysis, Elliptic, Nansen, Dune Analytics, DeBank, and Etherscan—focusing on two critical metrics: transaction accuracy and real-time responsiveness. Applying a mixed methods approach that combines a quantitative survey (n = 138) with qualitative interviews (n = 12), we identified critical platform features and found significant differences across these platforms with respect to compliance features, advanced analytics, and user experience. We used a utility-based model that links accuracy and responsiveness metrics, allowing us to adjust differing priorities and risk management needs for users. The results show the need for balanced, user-centric solutions that accommodate regulatory, technological efficiency and affordability requirements. Our study contributes to the growing knowledge base by providing a structured evaluation model and empirical insights, offering clear directions for practitioners, platform developers, and policymakers aiming to strengthen the DeFi ecosystem.

Purpose: This paper aims to focus on the need for an enhanced anti-money laundering (AML) regulation for decentralised finance (DeFi) to protect the integrity of global financial systems against illicit activities. Research highlights the requirement for a robust regulatory strategy for the fast-paced DeFi evolvement. Design/methodology/approach: This study used doctrinal legal research by analysing legislation, which involved creating use cases to illustrate different aspects of potential illicit activities via the DeFi ecosystem. Various DeFi applications were assessed for the potential regulatory responses and outcomes. Findings: This paper offers valuable insight into the regulatory challenges presented by DeFi. This study addresses the blind spots leveraged by criminals afforded by the DeFi’s decentralised nature. This paper offers a comprehensive examination of DeFi regulatory challenges based on use-case scenarios and provides recommendations for regulators on how to address them effectively. Originality/value: This paper proposes measures for regulatory authorities to minimise money laundering risks through new channels such as decentralised exchanges, non-custodial wallets and cross-chain bridges. This study concludes with the future directions for DeFi regulation and AML compliance.

This study focuses on the analysis of cryptocurrency regulatory developments in Europe. The aim is to review national crypto-legislation in Europe and the EU's latest initiative to create designated regulatory instruments for the crypto-economy. This study assessed whether the European Union's Regulation on Markets in Crypto-Assets (MiCA) would have the intended effect. Drawing on the results of a survey of crypto experts from five European countries, this study evaluated the effectiveness of current regulation across Europe and how it can be improved to reduce financial crimes. The findings show that a unified national legal framework for regulating transactions with crypto assets does not exist in European countries. Current crypto regulations are dictated by anti-money laundering recommendations. This study provides suggestions for improving MiCA regulation. The article offers recommendations for an international regulatory standard for crypto assets and insights for increasing efficiency in regulating DeFi, NFTs, and smart contracts.

Blockchain technology’s increasing adoption across diverse sectors necessitates robust security measures to mitigate rising fraudulent activities. This paper presents a comprehensive bibliometric analysis of anomaly detection research in blockchain networks from 2017 to 2024, conducted under the PRISMA paradigm. Using CiteSpace 6.4.R1, we systematically map the knowledge domain based on 363 WoSCC-indexed articles. The analysis encompasses collaboration networks, co-citation patterns, citation bursts, and keyword trends to identify emerging research directions, influential contributors, and persistent challenges. The study reveals geographical concentrations of research activity, key institutional players, the evolution of theoretical frameworks, and shifts from basic security mechanisms to sophisticated machine learning and graph neural network approaches. This research summarizes the state of the field and highlights future directions essential for blockchain security.

A surge of digital technologies adopted into Industrial Control Systems (ICS) exposes critical infrastructures to increasingly hostile and well‐organised cybercrime. The increased need for flexibility and convenient administration expands the attack surface. Likewise, an insider with authorised access reveals a difficult‐to‐detect attack vector. Because of the range of critical services that ICS provide, disruptions to operations could have devastating consequences making ICS an attractive target for sophisticated threat actors. Hence, the authors introduce a novel anomalous behaviour detection model for ICS data streams from physical plant sensors. A model for one‐class classification is developed, using stream rebalancing followed by adaptive machine learning algorithms coupled with drift detection methods to detect anomalies from physical plant sensor data. The authors’ approach is shown on ICS datasets. Additionally, a use case illustrates the model's applicability to post‐incident investigations as part of a defence‐in‐depth capability in ICS. The experimental results show that the proposed model achieves an overall Matthews Correlation Coefficient score of 0.999 and Cohen's Kappa score of 0.9986 on limited variable single‐type anomalous behaviour per data stream. The results on wide data streams achieve an MCC score of 0.981 and a K score of 0.9808 in the prevalence of multiple types of anomalous instances.

Facial expression recognition is the key area of research in computer vision, enabling intelligent devices to understand human emotions and intentions. However, recognition of facial expressions in natural scenes presents challenges due to environmental factors like occlusion and pose variations. To address this, we propose a novel approach that combines local feature enhancement and global information correlation. This method allows the model to learn both local and global facial features along with contextual information. By enhancing salient local features and exploring multi-scale facial expression features, our model effectively mitigates the impact of occlusion and pose variations, improving recognition accuracy. Experimental results demonstrate that our adapted model outperforms alternative algorithms in recognizing facial expressions under challenging environments, achieving recognition accuracies of 85.07% and 99.35% on the RAF-DB and CK+ datasets, respectively.