Edge data analytics refers to processing near data sources at the edge of the network to reduce delays in data transmission and, consequently, enable real-time interactions. However, data analytics at the edge introduces numerous security risks that can impact the data being processed. Thus, safeguarding sensitive data from being exposed to illegitimate users is crucial to avoiding uncertainties and maintaining the overall quality of the service offered. Most existing edge security models have considered attacks during data analysis as an afterthought. In this paper, an overview of edge data analytics in healthcare, traffic management, and smart city use cases is provided, including the possible attacks and their impacts on edge data analytics. Further, existing models are investigated to understand how these attacks are handled and research gaps are identified. Finally, research directions to enhance data analytics at the edge are presented.

In response to the escalating cyber threats, the efficiency of Cyber Threat Intelligence (CTI) data collection has become paramount in ensuring robust cybersecurity. However, existing works encounter significant challenges in preprocessing large volumes of multilingual threat data, leading to inefficiencies in real-time threat analysis. This paper presents a systematic review of current techniques aimed at enhancing CTI data collection efficiency. Additionally, it proposes a conceptual model to further advance the effectiveness of threat intelligence feeds. Following the PRISMA guidelines, the review examines relevant studies from the Scopus database, highlighting the critical role of artificial intelligence (AI) and machine learning models in optimizing CTI data preprocessing. The findings underscore the importance of AI -driven methods, particularly supervised and unsupervised learning, in significantly improving the accuracy of threat detection and event extraction, thereby strengthening cybersecurity. Furthermore, the study identifies a gap in the existing research and introduces XBC conceptual model integrating XLM-RoBERTa, BiGRU, and CRF, specifically developed to address this gap. This paper contributes conceptually to the field by providing a detailed analysis of current CTI data collection techniques and introducing an innovative conceptual model to enhance future threat intelligence capabilities.

The emergence and application of blockchain technology can provide a certain degree of solution to many problems in the agricultural sector, especially in the sub-sectors of agricultural supply chain management, agricultural product traceability, and agricultural finance. The development of blockchain technology has increased significantly, and the scope of application has continued to expand. For that, it becomes an effective tool to promote agricultural development in many countries, especially in agriculturally developed countries. This paper presents a systematic review of the current implementation of blockchain in agriculture. Furthermore, the paper critically assesses the limitations, constraints, and unresolved issues surrounding the integration of blockchain technology within agricultural systems. This review is a resource for researchers and stakeholders interested in the prospects and challenges of building a blockchain-based agricultural ecosystem. It sheds light on the transformation of blockchain technology while addressing the need for further research and development to unlock its full benefits within the dynamic landscape of agriculture.

Counterfeit products pose a growing concern for brand owners and consumers due to their adverse effects on user experience and brand value, leading to unsatisfied customers, negative publicity, and even potential health risks. This paper addresses the growing issues surrounding fake goods, which negatively affect customers and brand owners, including harmed user experiences, tarnished reputations, and potential health risks. The work proposed an encrypted QR code emphasising encryption techniques embedded in a mobile application with a friendly user interface and seamless interaction with existing authentication mechanisms to resolve this problem. The application aims to detect counterfeit goods, protect brand reputation, and increase consumer confidence by guaranteeing product authenticity. The study conducted a usability evaluation with 30 volunteers. The results indicated that enQRure proved to be a valuable and user-friendly application. Respondents expressed satisfaction with the application’s feature that facilitates product authentication. Feedback analysis on features offered by enQRure, such as product registration, product authentication, and encrypted QR code generation processes, revealed that most respondents found them straightforward and useful. They perceived enQRure as an effective means to authenticate products, aligning with their preferences and needs.