Secure software development is vital from both the developer and business perspectives. Although researchers have conducted studies examining specific aspects, no study has examined the overall status of, trends in and business focus regarding secure software development—a gap this study aims to fill. This study searched digital databases and identified 138 abstracts and 95 full articles. It conducted a scoping study and utilised NVivo software to generate themes, first from abstracts and then from source documents. It then analysed the top 25 themes to identify the current status of and trends in secure software development. The study then investigated the status of and trends in the business focus using the keyword ‘business’ alone. Results show a preponderance of themes that matched across the abstract and document analyses, with the most commonly discussed themes activities or processes related to secure software development. Although not present in the abstracts, source document analysis identified cybersecurity as one of the top 25 themes. There was minimal discussion on business focus, although it showed an upward trend over the project period. Journal articles and conference papers shared similar themes of significance; the book chapter themes differed, with cybersecurity the most frequently discussed theme.

Phishing attacks continue to grow in scale and sophistication, causing substantial financial losses and privacy breaches worldwide. Recent advances in large language models (LLMs) have brought significant changes to the generation and detection of phishing content. This study systematically investigates the dual role of LLMs in facilitating phishing attacks and strengthening countermeasures. Using the PRISMA methodology, authors screened 142 records published between January 2023 and April 2025 and identified 36 eligible studies from major academic databases, including IEEE Xplore, ScienceDirect, ACM Digital Library, Web of Science, and Scopus. A comprehensive and rigorous analysis was conducted of research trends/themes over time, dataset characteristics, and the LLM architectures/models employed. The findings reveal that most studies relied on manually generated datasets rather than publicly available benchmark datasets, and that GPT-based models received considerably more attention than other LLM architectures. The review demonstrates that LLMs substantially enhance the generation of phishing content by producing coherent, contextually relevant, and persuasive email and website content. This capability lowers the technical barrier for attackers and potentially increases attack effectiveness. Conversely, LLMs also strengthen defensive strategies by enabling more effective analysis of textual and visual content for phishing detection. In many cases, LLM-based approaches outperform traditional machine learning and deep learning methods and, in certain contexts, approach or match human-level performance. Overall, the findings suggest that LLMs have accelerated and automated phishing-related processes, simultaneously intensifying the threat landscape and advancing defensive capabilities. • The first in-depth study to review LLMs usage in Phishing attack generation and detection. • The study reveals LLMs have accelerated and automated phishing-related processes, elevating both threats and defence mechanisms. • GenAI-based multimodal phishing attacks are on the rise due to the wider adoption of GenAI tools in general.

Clustering is a basic data mining operation that groups data points with similar inherent structure. Among clustering techniques, Density Peak Clustering (DPC) is notable for detecting clusters of arbitrary shapes without requiring the number of clusters in advance. However, DPC suffers from parameter-sensitive local density estimation, inadequate handling of noise and boundary points, and rigid binary cluster assignments. To overcome these limitations, fuzzy logic is often embedded in DPC, but the selection of the most appropriate membership function is still an open research question. In this paper, we propose an enhanced DPC variant with three methodological improvements: (i) substituting cutoff distance-based density estimation with a K-Nearest Neighbors (KNN) functioned kernel to achieve stable and robust density estimation, (ii) adding a noise parameter Lambda ( λ ) to better identify and distinguish noise and boundary points, and (iii) using fuzzy membership functions to allow for probabilistic and soft cluster assignments of data points. Comparative analyses of four fuzzy membership functions (i.e., Gaussian, Combination Gaussian, Trapezoidal, and Triangular) were performed on real, synthetic, and high-dimensional datasets. The experimental results indicate that the proposed algorithm with the Gaussian fuzzy membership function outperforms other membership functions and traditional clustering algorithms including the original DPC, FKNN, and DPC-DBFN in terms of clustering accuracy, stability, adaptability, and noise resistance, and is therefore part of our final suggested method. The proposed method overall relieves original DPC shortcomings by strengthening adaptability, accuracy, and applicability to complex real-world clustering tasks. • Substituting standard cutoff-distance-based kernel with KNN for enhanced clustering. • Introducing λ parameter to manage noise and boundary points for separating clusters. • Introducing fuzzy neighborhood kernel for soft cluster membership assignment. • Investigating the impact of several fuzzy membership functions on clustering. • Proposed strategy outperforms original DPC, FKNN, and DPC-DBFN on datasets.