In this study, we analyzed bitcoin blockchain data for the period between 2013 and 2018. We constructed daily networks and analyzed the network properties of the bitcoin users and bitcoin flow attributed as edge flow circulated between users to focus on weekdays and weekends activities. In the real world, businesses take time off, particularly on weekends. This is no different in the crypto-asset world, which, theoretically, is operable 24/7. We also performed a threshold analysis of the flow of bitcoin to identify the big wallets and to compare it with the identity of real-world crypto-exchange companies, in particular, their weekly patterns. Finally, we propose a methodology to identify the financial institution in the bitcoin blockchain on the basis of fulfilling some key criteria. The criteria are having high frequency, appearing persistently on daily big trades and showing a distinct weekly pattern of total average network flow.

A correction to this paper has been published: https://doi.org/10.1007/s40844-021-00216-2

Phishing attacks have continued to increase globally over the past several decades and remain a major entry point for multi-vector cyberattacks. As conventional single-method detection techniques often struggle to identify increasingly sophisticated phishing attempts, researchers have increasingly focused on AI-based hybrid detection approaches. This systematic review, conducted in accordance with the PRISMA 2020 statement, provides a focused synthesis of these approaches. A comprehensive search was conducted across seven academic databases and one scholarly search engine. Three reviewers independently screened the records, extracted the data, and conducted the methodological quality assessment, with disagreements resolved through discussion and consensus. Following a multi-stage screening process, 18 peer-reviewed studies published between January 2020 and June 2026 were included. The methodological quality of each study was evaluated using a structured quality-assessment checklist. The included studies were synthesized narratively with emphasis on hybridization techniques, data modalities, model architectures, evaluation metrics, strengths, limitations, and future research directions. The findings indicate that AI-based hybrid approaches generally achieved strong benchmark performance and improved robustness against the phishing scenarios evaluated within the included studies. However, limitations were identified in relation to explainability, generalizability, computational complexity, and real-time deployment. The review also identified continuing challenges involving the trade-off between accuracy and efficiency, dependence on unimodal or benchmark datasets, limited reproducibility reporting, and insufficient real-world validation. Future research should prioritize lightweight and fairness-aware hybrid architectures, multimodal data integration, continual learning, and stronger model interpretability to support reliable and scalable deployment in real-world environments.