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The Unified Theory of Acceptance and Use of Technology (UTAUT) helps to identify the drivers of user acceptance of new information technology. Based on this concept, four categories are important to direct predictors of user behavior and acceptance. These predictors are effort expectancy, performance expectancy, social influence, and facilitators. Although various scholars have discussed the importance of emotional expectancy in user acceptance of new technologies, it is not a direct determinant in the UTAUT model. This study argues that emotional expectancy is a direct determinant of behavioral intention. Therefore, this study employed a quantitative methodology using a questionnaire distributed via email and WhatsApp to investigate the effect of emotional expectancy on behavioral intention to adopt blockchain technology among Nigerians. In doing so, we extended the UTAUT model by integrating emotional expectancy as the fifth construct of the model. The user model has not been extensively tested in developing countries; therefore, this research could guide planners to reflect on the characteristics that contribute to the successful adoption of blockchain technology for specific user groups. The results of this research could provide blockchain users with useful insights into user perceptions and behaviors toward blockchain technology, from a user perspective. [ABSTRACT FROM AUTHOR]

In today's digital landscape, cybercriminals are constantly evolving their tactics, making it challenging for traditional cybersecurity methods to keep up. To address this issue, this study explores the potential of knowledge graph reasoning as a more adaptable and sophisticated approach to identify and counter network attacks. By leveraging graph structures imbued with human‐like thinking, this method enhances the resilience of cybersecurity systems. The study focuses on three critical aspects: data preparation, semantic foundations, and knowledge graph inference techniques. Through an in‐depth analysis of these components, the research aims to reveal how knowledge graph reasoning can improve cyberattack detection and enhance the overall efficacy of cybersecurity measures, including intrusion detection systems. The proposed approach has undergone extensive experimentation to validate its effectiveness compared to existing methods. The results of the experiment have shown a remarkable advancement in accuracy, speed, and recall for recognition, surpassing current methods. This achievement is a notable contribution in the realm of managing big data in cybersecurity. The study establishes a foundation for the automation of network attack detection, ultimately enhancing overall network security. [ABSTRACT FROM AUTHOR]

Intrusion Detection System (IDS) is a critical cybersecurity task that involves monitoring network traffic for malicious activity and taking appropriate action to stop it. However, insufficient training data or improperly chosen thresholds often limit the accuracy of such systems, resulting in high false‐positive rates. To improve the accuracy of an IDS, blockchain technology can be used as it provides a secure, decentralized, immutable ledger that can track suspicious activity over time and also identify intrusions globally. In this paper, the authors propose a novel methodology to improve the accuracy of blockchain‐based IDS. The approach combines different intrusion detection algorithms using a blockchain‐integrated architecture. It is based on the fusion principle and weighted votes, which the authors used to determine their results. The authors tested the system on DARPA 99 and MIT‐Lincoln Labs datasets using accuracy and false‐positive rate as their two metrics. The system achieved 92.6% accuracy and 7.4% false‐positive rates, indicating that the proposed system significantly increases the accuracy while reducing the false‐positive rate, opening up new opportunities for the development of highly accurate networks. In this paper, the authors propose a novel methodology to improve the accuracy of blockchain‐based intrusion detection and prevention systems, which is based on combining different intrusion detection algorithms and using a blockchain‐integrated architecture. The authors tested the system on DARPA 99 and MIT‐Lincoln Labs datasets using accuracy and false‐positive rate as their two metrics. The system achieved 92.6% accuracy and 7.4% false‐positive rate, indicating that the proposed system significantly increases the accuracy while reducing the false‐positive rate, opening up new opportunities for the development of highly accurate networks. [ABSTRACT FROM AUTHOR]