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- BIBLIOMETRIC STUDY ON THE DEVELOPMENT AND IMPLEMENTATION OF CYBERSECURITY IN AUTONOMOUS VEHICLESPublication . Teixeira, Henrique; Lousã, Mário Jorge Dias; Morais, José C.The main objective was to examine the trajectory of scientific research in this domain, identify the most influential publications related to cybersecurity in autonomous vehicles and pinpoint research opportunities, supported by the PRISMA method. Additionally, the study explores cybersecurity themes in autonomous vehicles, emphasizing the significance of concepts like blockchain, machine learning, and deep learning essential in formulating business strategies. Furthermore, the research identifies influential scientific publications, predominant journals, the most productive countries, and authors with the most publications on cybersecurity in autonomous vehicles. It identifies research opportunities organized into two distinct clusters to provide a comprehensive understanding of the current state of research in this field and offer insights for companies and academics interested in contributing to future advancements in the cybersecurity of autonomous vehicles. The article demonstrates that cybersecurity is a fundamental area for the development and implementation of secure and reliable autonomous vehicles.
- BIBLIOMETRIC STUDY ON THE IMPORTANCE OF ENDPOINT SECURITY IN COMPANIESPublication . Gonçalves, João; Lousã, Mário Jorge Dias; Morais, José C.This bibliometric study addresses the importance of endpoint security in companies, considering the growing use of information technologies, both in business and personal use. It highlights the need to protect endpoints such as computers, mobile devices, servers, and IoT devices. Endpoint security encompasses measures such as monitoring the files and binaries on and running on the machine using antivirus, data encryption, and threat detection solutions. The literature review highlights the importance of terminology and best practices, highlighting the application of graph-based approaches to strengthen security in medical information networks. Tools such as EDR are cited as essential, especially for small and medium-sized companies. The study emphasizes the importance of business continuity in the face of cyber threats, highlighting the role of artificial intelligence, machine learning, and frameworks. It takes a bibliometric approach, using a specific database to collect bibliometric data on scientific publications published between 2017 and 2023. As a basis for the study, the words “cybersecurity”, “endpoint security”, “business continuity”, and “business” were used. Various analyses of bibliometric results are also presented, including the number of publications by type of document, the scientific journals with the highest number of publications, the countries with the highest number of publications, the number of publications per author, the most cited articles, and the occurrence of identified keywords.
- BIBLIOMETRIC STUDY ON THE IMPORTANCE OF ENDPOINT SECURITY IN COMPANIESPublication . Gonçalves, João; Lousã, Mário Jorge Dias; Morais, José C.This bibliometric study addresses the importance of endpoint security in companies, considering the growing use of information technologies, both in business and personal use. It highlights the need to protect endpoints such as computers, mobile devices, servers, and IoT devices. Endpoint security encompasses measures such as monitoring the files and binaries on and running on the machine using antivirus, data encryption, and threat detection solutions. The literature review highlights the importance of terminology and best practices, highlighting the application of graph-based approaches to strengthen security in medical information networks. Tools such as EDR are cited as essential, especially for small and medium-sized companies. The study emphasizes the importance of business continuity in the face of cyber threats, highlighting the role of artificial intelligence, machine learning, and frameworks. It takes a bibliometric approach, using a specific database to collect bibliometric data on scientific publications published between 2017 and 2023. As a basis for the study, the words “cybersecurity”, “endpoint security”, “business continuity”, and “business” were used. Various analyses of bibliometric results are also presented, including the number of publications by type of document, the scientific journals with the highest number of publications, the countries with the highest number of publications, the number of publications per author, the most cited articles, and the occurrence of identified keywords.
- BIBLIOMETRIC ANALYSIS ON CYBERSPACE SECURITY - NIS DIRECTIVESPublication . Cláudia, Borgguen; Morais, José C.; Lousã, Mário Jorge DiasThe impact of security in cyberspace has been increasing, motivating companies to reconsider their security strategies. In addition, people from various countries who are aware of this growth are seeking to present studies in various journals that allow them to identify elements that contribute to the consolidation of the concept of security in cyberspace. With this reality in mind, this study, supported by a bibliometric analysis of security in cyberspace based on articles published in the last eight years, aims to analyze the evolution of scientific research, identify the most influential scientific publications on topics related to cyberspace security, and detect research opportunities in the field. The study also discusses the implementation of the legal framework for security in cyberspace and the NIS Directive, aspects that European companies should consider in their cybersecurity strategy. The study's conclusions highlight the multifaceted nature of cybersecurity challenges and the need for a holistic and collaborative approach to strengthening digital resilience, with an emphasis on promoting a culture of awareness encouraged at the organizational and social level by policymakers, industry leaders, and researchers.
- ARTIFICIAL INTELLIGENCE–BASED SUPER NODES FOR REAL-TIME THREAT DETECTION IN DISTRIBUTED ENVIRONMENTS BIBLIOMETRIC ANALYSISPublication . Lopes, José; Dias Lousã, Mário Jorge; Dias Lousã, Mário Jorge; Pereira de Morais, José Carlos; Pereira de Morais, José Carlos; Morais, José Carlos; Lousã, MárioThe widespread adoption of distributed systems, driven by the growth of the Internet of Things (IoT), edge computing, and cloud infrastructure, has substantially expanded the attack surface of modern digital ecosystems. These environments, characterized by high heterogeneity, large data volumes, and stringent latency requirements, make real-time threat detection a complex task. Traditional, pre-dominantly centralized security mechanisms reveal clear limitations in scalability and response time in the face of increasingly dynamic attack patterns. In this context, Artificial Intelligence (AI) and Machine Learning have emerged as essential enablers for more effective intrusion detection. At the same time, the concept of “super nodes” is gaining prominence: strategically positioned network elements with enhanced computational capabilities that act as intelligent intermediaries between edge devices and the central cloud. This study presents a bibliometric analysis of the use of AI-based super nodes for real-time threat detection. The analysis focuses on a sample of 300 publications indexed in the Lens.org database (2015–2025), selected according to the PRISMA 2020 guidelines. Through descriptive indicators and network analysis (such as keyword co-occurrence), research trends, the-matic structures, and emerging directions in this field are identified.
- SECURITY AND RISK IN SOFTWARE DEVELOPMENT PROJECTS: A BIBLIOMETRIC REVIEWPublication . Conceição, Francisco; Dias Lousã, Mário Jorge; Dias Lousã, Mário Jorge; Pereira de Morais, José Carlos; Pereira de Morais, José Carlos; Morais, José Carlos; Lousã, MárioSecurity analysis is increasingly central to software development as organizations face rising cyber risk and regulatory pressure. Although extensive research exists on cyber risk assessment, secure software development, and security requirements, the literature remains fragmented at the project level. This study presents a bibliometric analysis of research published between 2015 and 2026, using data retrieved exclusively from The Lens and structured through a PRISMA-guided workflow. Only journal and conference publications addressing security analysis within software development pro-jects were retained, while studies focused solely on isolated technical vulnerabilities or non-project contexts were excluded, resulting in a final dataset of 1,008 documents. The dataset was analyzed using descriptive bibliometrics, collaboration and geographical analysis, field-of-study classifica-tion, keyword co-occurrence, co-citation, and bibliographic coupling, supported by VOSviewer. Re-sults show sustained growth after 2019 and strong dominance of computer science and software en-gineering. Influential contributions cluster around secure SDLC frameworks, ISO/NIST standards, requirement decomposition, and emerging quantitative risk models. Despite this consolidation, the analysis reveals persistent gaps, including weak integration between cyber risk assessment and re-quirements engineering, limited project-level operationalization of security attributes, and a scarcity of approaches tailored to small and medium-sized enterprises (SMEs). These findings highlight the need for integrated, requirement-driven security analysis frameworks that bridge technical and or-ganizational perspectives within software development projects.
- SECURITY AND PRIVACY IN EXPLAINABLE AI: A BIBLIOMETRIC ANALYSIS OF EMERGING LEAKAGE RISKSPublication . Matos, Mafalda; Dias Lousã, Mário Jorge; Dias Lousã, Mário Jorge; Pereira de Morais, José Carlos; Pereira de Morais, José Carlos; Morais, José Carlos; Lousã, MárioExplainable Artificial Intelligence (XAI) has gained increasing attention as a means of improving the transparency and trustworthiness of machine learning algorithms, particularly in domains where security and privacy concerns are relevant. This study presents a bibliometric analysis of research at the intersection of explainable artificial intelligence, security, and privacy. The aim was to charac-terize publication trends, thematic structures, and keyword relationships within the field. Scholarly records were retrieved from the Lens database using a structured search strategy based on the PRISMA protocol and analyzed using bibliometric tools, including Bibliometrix and VOSviewer. The total number of studies analyzed was 8,099, and the analyzed time frame was 2010–2025. The analysis examined general publication information, annual scientific production, leading publication venues, and keyword co-occurrence networks. Results indicate a rapid growth in XAI-related publi-cations in recent years and reveal several major thematic clusters, including deep learning–driven medical imaging applications, foundational machine learning and data science concepts, explaina-bility methods in security and distributed learning contexts, and governance-oriented themes related to ethics, privacy, and trust. Overall, the findings highlight the application-driven and interdiscipli-nary nature of explainable AI research, while showing that security and privacy topics, although present, remain relatively peripheral within the broader XAI literature.
- GENERATIVE AI MUTABILITY IN CYBERSECURITY: A BIBLIOMETRIC REVIEWPublication . Oliveira, Pedro; Dias Lousã, Mário Jorge; Dias Lousã, Mário Jorge; Pereira de Morais, José Carlos; Pereira de Morais, José Carlos; Morais, José Carlos; Lousã, MárioThe expansion of generative AI (GenAI) is forcing us to rethink cybersecurity, expanding both de-fensive automation and scalable offensive techniques. This bibliometric review maps the change driven by GenAI in cybersecurity through a PRISMA-guided selection of 154 documents from The Lens (20 December 2025). The current state is summarized by scientific mapping results (co-author-ship, co-word, and co-citation networks, and thematic evolution) to identify dominant architectures, thematic clusters, and collaboration patterns, and implications for governance and auditing. We note the exponential growth of publications in 2022. We notice the trend. The authors group publications into several architectures: large language models (LLMs), generative networks (GANs), and diffu-sion models. These focus on common topics, (i) large-scale phishing and social engineering, (ii) mutability, obfuscation, and adversarial evasion of malware, and (iii) intrusion detection and cyber threat intelligence using synthetic data. Co-citation networks and keywords show that adversarial robustness, red teaming, and benchmarking are interconnected. We find that explainability and hu-man-in-the-loop defense exist as minor but growing topics. One risk is the BlackMamba case, which transmits an LLM-assisted pipeline capable of generating more than 10,000 semantically identical but structurally distinct mutations per hour and achieving a 98.2% evasion rate against commercial EDR solutions. Risk mitigation should prioritize benchmarking and standardized reporting, continu-ous red teaming, and telemetry monitoring, incorporated into dynamic audit frameworks, supported by explicit international governance for high-risk GenAI cybersecurity applications.
- SECURITY AUTOMATION AND VULNERABILITY ANALYSIS IN .NET APPLICATIONS: A BIBLIOMETRIC REVIEWPublication . Pinto, Rúben; Lousã, Mário Dias; Dias Lousã, Mário Jorge; Morais, José Carlos; Pereira de Morais, José Carlos; Morais, José Carlos; Lousã, Mário DiasApplication security is a crucial requirement in modern software, but manually detecting vulnerabil-ities is time-consuming and prone to failure. In the context of .NET platforms, widely used in corpo-rate development, automation of security checks and vulnerability analysis emerges as a promising approach to efficiently mitigate risks. This article presents a bibliometric review on “Security Auto-mation and Vulnerability Analysis in .NET Applications”, identifying research trends and existing gaps. Publications indexed in The Lens database (2004–2025) were analyzed following PRISMA criteria, complemented by bibliometric techniques (co-authorship, co-citation, and co-occurrence of keywords) using tools such as VOSviewer. The results reveal a significant growth in work in the last decade, mainly addressing web vulnerabilities (e.g., SQL injection and XSS), as well as recent ap-proaches to machine learning. However, there are important gaps, including the scarcity of studies specifically focused on the .NET ecosystem and low levels of collaboration among researchers. In short, although security automation has advanced, there are still research opportunities to fill the identified gaps, namely by adapting and expanding techniques for the .NET context.
- INTEGRATION OF AES AND BLOCKCHAIN FOR SENSITIVE DATA PROTECTION: A BIBLIOMETRIC ANALYSISPublication . Gomes, Filipe; Dias Lousã, Mário Jorge; Dias Lousã, Mário Jorge; Pereira de Morais, José Carlos; Pereira de Morais, José Carlos; Morais, José Carlos; Lousã, MárioThe protection of sensitive data is becoming increasingly complex as digital services expand, con-nected devices multiply, and distributed systems become the norm. Encryption methods such as the Advanced Encryption Standard (AES) remain fundamental to ensuring the confidentiality of infor-mation, but they do not meet all security requirements. At the same time, blockchain technology has been adopted in various contexts for its ability to ensure integrity, traceability, and non-repudiation. Despite the complementary nature of these technologies, studies analyzing their combined use re-main relatively scarce and fragmented. This article analyses existing research on the combined use of blockchain and encryption through a bibliometric analysis of scientific publications indexed in The Lens database between 2016 and 2026. The study is based on descriptive indicators and keyword co-occurrence analysis, using the VOSviewer tool to identify thematic relationships and research trends. Results show significant growth from 2020, with major contributions from Asia and increas-ing interest across multiple disciplinary areas. Most publications are situated within computer science and cybersecurity, while applied research is primarily focused on domains such as healthcare and Internet of Things (IoT) systems. Despite this expansion, the literature remains largely fragmented, with relatively few studies proposing or experimentally evaluating integrated architectures that ef-fectively combine encryption and blockchain mechanisms to simultaneously ensure data confidenti-ality, integrity, and auditability.