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The internet has completely merged into contemporary life. People are addicted to using internet services for everyday activities. Consequently, an abundance of information about people and organizations is available online, which encourages the proliferation of cybercrimes. Cybercriminals often use malicious links for large-scale cyberattacks, which are disseminated via email, SMS and social media. Recognizing malicious links online can be exceedingly challenging. The purpose of this paper is to present a strong security system that can detect malicious links in the cyberspace using natural language processing technique.

The researcher recommends a variety of approaches, including blacklisting and rules-based machine/deep learning, for automatically recognizing malicious links. But the approaches generally necessitate the generation of a set of features to generalize the detection process. Most of the features are generated by processing URLs and content of the web page, as well as some external features such as the ranking of the web page and domain name system information. This process of feature extraction and selection typically takes more time and demands a high level of expertise in the domain. Sometimes the generated features may not leverage the full potentials of the data set. In addition, the majority of the currently deployed systems make use of a single classifier for the classification of malicious links. However, prediction accuracy may vary widely depending on the data set and the classifier used.

To address the issue of generating feature sets, the proposed method uses natural language processing techniques (term frequency and inverse document frequency) that vectorize URLs. To build a robust system for the classification of malicious links, the proposed system implements weighted soft voting classifier, an ensemble classifier that combines predictions of base classifiers. The ability or skill of each classifier serves as the base for the weight that is assigned to it.

The proposed method performs better when the optimal weights are assigned. The performance of the proposed method was assessed by using two different data sets (D1 and D2) and compared performance against base machine learning classifiers and previous research results. The outcome accuracy shows that the proposed method is superior to the existing methods, offering 91.4% and 98.8% accuracy for data sets D1 and D2, respectively.

In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature.

Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness.

A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information.

This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries.

It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront.

Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life.

Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey.

Social media is replete with malicious and unempathetic rhetoric yet few studies explain why these emotions are publicly dispersed. The purpose of the study is to investigate how the intergroup counter-empathic response called schadenfreude originates and how it prompts media consumption and engagement.

The study consists of two field surveys of 635 in-group members of two professional sports teams and 300 residents of California and Texas with political party affiliations. The analysis uses SEM quantitative methods.

Domain passion and group identification together determine the harmonious/obsessive tendencies of passion for an activity and explain the schadenfreude response toward the rival out-group. Group identification is a stronger driver of obsessive passion compared to harmonious passion. Schadenfreude directly influences the use of traditional media (TV, radio, domain websites), it triggers social media engagement (posting), and it accelerates harmonious passion's effects on social media posting.

The study is limited by the groups used to evaluate the research model, sports, and politics.

The more highly identified and passionate group members experience greater counter-empathy toward a rival. At extreme levels of group identification, obsessive passion increases at an increasing rate and may characterize extremism. Harboring feelings of schadenfreude toward the out-group prompts those with harmonious passion for an activity to more frequently engage on social media in unempathetic ways.

This study links the unempathetic, yet common emotion of schadenfreude with passion, intergroup dynamics, and media behavior.

This study aims to investigate how phishers apply persuasion principles and construct deceptive URLs in mobile instant messaging (MIM) phishing.

In total, 67 examples of real-world MIM phishing attacks were collected from various online sources. Each example was coded using established guidelines from the literature to identify the persuasion principles, and the URL construction techniques employed.

The principles of social proof, liking and authority were the most widely used in MIM phishing, followed by scarcity and reciprocity. Most phishing examples use three persuasion principles, often a combination of authority, liking and social proof. In contrast to email phishing but similar to vishing, the social proof principle was the most commonly used in MIM phishing. Phishers implement the social proof principle in different ways, most commonly by claiming that other users have already acted (e.g. crafting messages that indicate the sender has already benefited from the scam). In contrast to email, retail and fintech companies are the most commonly targeted in MIM phishing. Furthermore, phishers created deceptive URLs using multiple URL obfuscation techniques, often using spoofed domains, to make the URL complex by adding random characters and using homoglyphs.

The insights from this study provide a theoretical foundation for future research on the psychological aspects of phishing in MIM apps. The study provides recommendations that software developers should consider when developing automated anti-phishing solutions for MIM apps and proposes a set of MIM phishing awareness training tips.

Wikipedia's inclusive editorial policy permits unrestricted participation, enabling individuals to contribute and disseminate their expertise while drawing upon a multitude of external sources. News media outlets constitute nearly one-third of all citations within Wikipedia. However, embracing such a radically open approach also poses the challenge of the potential introduction of biased content or viewpoints into Wikipedia. The authors conduct an investigation into the integrity of knowledge within Wikipedia, focusing on the dimensions of source political polarization and trustworthiness. Specifically, the authors delve into the conceivable presence of political polarization within the news media citations on Wikipedia, identify the factors that may influence such polarization within the Wikipedia ecosystem and scrutinize the correlation between political polarization in news media sources and the factual reliability of Wikipedia's content.

The authors conduct a descriptive and regression analysis, relying on Wikipedia Citations, a large-scale open dataset of nearly 30 million citations from English Wikipedia. Additionally, this dataset has been augmented with information obtained from the Media Bias Monitor (MBM) and the Media Bias Fact Check (MBFC).

The authors find a moderate yet significant liberal bias in the choice of news media sources across Wikipedia. Furthermore, the authors show that this effect persists when accounting for the factual reliability of the news media.

The results contribute to Wikipedia’s knowledge integrity agenda in suggesting that a systematic effort would help to better map potential biases in Wikipedia and find means to strengthen its neutral point of view policy.

This study aims to highlight the importance of using attribution theory in metaverse tourism research. The study addresses the lack of clarity regarding the attribution theory’s dimensions (locus, stability, controllability) potential application in the metaverse tourism context.

This study uses a comprehensive exploration of the research gap by searching top-tier journals in Scopus and Web of Science databases about the relevant literature to analyze relevant data to provide a foundation for future transformative research. The study examines the relationship between attribution theory and metaverse tourism and explores how attribution theory can inform the understanding of tourists’ judgments and actions in the metaverse context.

The study shows that attribution theory has the potential to significantly improve the understanding of metaverse tourism by illuminating tourists’ decision-making processes and the factors contributing to those decisions. The study highlights the importance of applying attribution theory to generate more impactful and reliable implications for the tourism industry.

This study is transformative because it provides a foundational understanding of the application of attribution theory in metaverse tourism research. The study is significant because it sheds light on an underexplored area where the theoretical framework is necessary to inform and guide tourism technology research. The study’s originality lies in its contribution to tourism by identifying room for improvement in metaverse tourism research and highlighting the potential benefits of using attribution theory.

本研究通过在Scopus和Web of Science数据库中搜索相关文献的顶级期刊, 对研究空白进行了全面探索, 以分析相关数据, 为未来的变革性研究提供基础。本研究考察了归因理论与元宇宙旅游之间的关系, 并探讨了归因理论如何在元宇宙背景下为理解游客的判断和行为提供信息。

本研究强调了归因理论在元宇宙旅游研究中的重要性。该研究解决了归因理论在元宇宙旅游背景下的潜在应用维度（轨迹、稳定性、可控性）缺乏明确性的问题。

研究表明, 归因理论有可能通过阐明游客的决策过程和促成这些决策的因素, 显著提高对元宇宙旅游的理解。该研究强调了应用归因理论对旅游业产生更具影响力和可靠性的影响的重要性。

本研究具有变革性, 因为它为归因理论在元宇宙旅游研究中的应用提供了基础性的理解。这项研究意义重大, 因为它揭示了一个研究不足的领域。在此领域, 理论框架是为旅游技术研究提供信息和指导所必需的。本研究的独创性在于它对旅游业的贡献, 它确定了元宇宙旅游研究的改进空间, 并强调了利用归因理论的潜在好处。

Este estudio se basa en una exploración exhaustiva de la brecha de investigación mediante la búsqueda de revistas de primer nivel en las bases de datos Scopus y Web of Science sobre la literatura relevante para analizar los datos relevantes con el fin de proporcionar una base para futuras investigaciones transformadoras. El estudio examina la relación entre la teoría de la atribución y el metaverso turístico y explora cómo la teoría de la atribución puede facilitar la comprensión de las evaluaciones y acciones de los turistas en el contexto metaverso.

Este estudio pone de relieve la importancia de utilizar la teoría de la atribución en la investigación turística en metaverso. El estudio aborda la falta de claridad sobre las dimensiones de la teoría de la atribución (lugar de causalidad, estabilidad y, controlabilidad) y su posible aplicación en el contexto del turismo en metaverso.

El estudio muestra que la teoría de la atribución tiene el potencial de mejorar de forma significativa la comprensión del metaverso turístico al esclarecer los procesos de toma de decisiones de los turistas y los factores que contribuyen a dichas decisiones. El estudio destaca la importancia de aplicar la teoría de la atribución para generar implicaciones más impactantes y fiables para la industria turística.

Este estudio es transformador porque proporciona una comprensión fundacional de la aplicación de la teoría de la atribución en la investigación del metaverso turístico. El estudio es significativo porque arroja luz sobre un ámbito poco explorado cuyo marco teórico es necesario para informar y orientar la investigación sobre tecnología turística. La originalidad del estudio radica en su contribución al turismo al identificar las posibilidades de mejora en la investigación del metaverso turístico y destacar los beneficios potenciales de la utilización de la teoría de la atribución.

Despite ongoing reports of insider-driven leakage of confidential data, both academic scholars and practitioners tend to focus on external threats and favour information technology (IT)-centric solutions to secure and strengthen their information security ecosystem. Unfortunately, they pay little attention to human resource management (HRM) solutions. This paper aims to address this gap and proposes an actionable human resource (HR)-centric and artificial intelligence (AI)-driven framework.

The paper highlights the dangers posed by insider threats and presents key findings from a Leximancer-based analysis of a rapid literature review on the role, nature and contribution of HRM for information security, especially in addressing insider threats. The study also discusses the limitations of these solutions and proposes an HR-in-the-loop model, driven by AI and machine learning to mitigate these limitations.

The paper argues that AI promises to offer many HRM-centric opportunities to fortify the information security architecture if used strategically and intelligently. The HR-in-the-loop model can ensure that the human factors are considered when designing information security solutions. By combining AI and machine learning with human expertise, this model can provide an effective and comprehensive approach to addressing insider threats.

The paper fills the research gap on the critical role of HR in securing and strengthening information security. It makes further contribution in identifying the limitations of HRM solutions in info security and how AI and machine learning can be leveraged to address these limitations to some extent.

The purpose of this study is to increase awareness of current supply chain (SC) security-related issues by providing an extensive analysis of existing SC security solutions and their limitations. The security of SCs has received increasing attention from researchers, due to the emerging risks associated with their distributed nature. The increase in risk in SCs comes from threats that are inherently similar regardless of the type of SC, thus, requiring similar defence mechanisms. Being able to identify the types of threats will help developers to build effective defences.

In this work, we provide an analysis of the threats, possible attacks and traceability solutions for SCs, and highlight outstanding problems. Through a comprehensive literature review (2015–2021), we analysed various SC security solutions, focussing on tracking solutions. In particular, we focus on three types of SCs: digital, food and pharmaceutical that are considered prime targets for cyberattacks. We introduce a systematic categorization of threats and discuss emerging solutions for prevention and mitigation.

Our study shows that the current traceability solutions for SC systems do not offer a broadened security analysis and fail to provide extensive protection against cyberattacks. Furthermore, global SCs face common challenges, as there are still unresolved issues, especially those related to the increasing SC complexity and interconnectivity, where cyberattacks are spread across suppliers.

This is the first time that a systematic categorization of general threats for SC is made based on an existing threat model for hardware SC.

In response to the growing interest in Internet of Things (IoT) technology and its profound implications for businesses and individuals, this bibliometric study focuses on a critical yet understudied aspect, i.e. cybersecurity. As IoT adoption grows, so do concerns regarding user privacy and data security. This study aims to provide a comprehensive understanding of the current research in this vital area, shedding light on research trends, gaps and emerging themes.

The study conducted a bibliometric analysis and systematic review of literature spanning over two decades (2013–2023). Bibliometric analysis is conducted using Biblioshiny which is R-software-based advanced analytical tool. Further, VOSviewer is used to conduct network analysis. The study highlights the evolving landscape of IoT cybersecurity, emphasizing interdisciplinary intersections and the ethical dimensions of IoT technologies.

The study uncovers crucial concerns related to IoT adoption, emphasizing the urgent need for comprehensive cybersecurity protocols. It identifies emerging themes such as artificial intelligence and blockchain integration, indicating a shift toward interdisciplinary solutions. Furthermore, the research highlights ethical gaps in current IoT discussions, emphasizing the importance of responsible innovation.

Businesses can bolster their cybersecurity strategies, policymakers can craft informed regulations and researchers are encouraged to explore IoT’s ethical dimensions.

This study pioneers a nuanced analysis of IoT cybersecurity, filling a crucial gap in the existing business and management literature. By synthesizing a decade of scholarly work, it provides foundational insights for researchers, businesses and policymakers. The research not only informs academic discourse but also offers practical guidance for enhancing IoT security measures and fostering ethical innovation.

This study aims to give a glimpse of the existing blockchain applications across industries and add to a complete knowledge of the blockchain’s properties.

Systematic literature review is used as the research strategy for this investigation and other aspects of the preferred reporting items for systematic reviews and meta-analyses framework have been incorporated to create a scholarly publications evaluation of the blockchain-based application in the financial arena and its future. The research looks at 86 studies published between 2018 and 2022.

There has been a steady but noticeable increase in the study of blockchain’s potential in many application domains over the past few of years. This rising tendency illustrates the newness and potential of blockchain technology, as well as the increasing attention from academics. According to the findings, blockchain is an appropriate solution for processing transactions using cryptocurrencies; nevertheless, it still has significant technical issues and limits that require to be exploring and solving before it can be considered a viable option. It is therefore, necessary to have a high level of reliability for payments and confidentiality, in addition to maintaining the anonymity of nodes, to stop assaults and efforts to disrupt transactions in the blockchain.

This study has several important theoretical and practical implications. First, it adds to the body of knowledge on blockchain and Fintech, focusing on the transaction side. While much blockchain research has focused on how the technology may affect strategic choices, this study has shed light on its potential from the perspective of financial reporting. Second, by highlighting the importance of the demand for the prompt identification of losses, this work adds to the body of knowledge on the factors that influence transaction frauds involving paper money. Additionally, by establishing the link between transparency and virtual transactions, the author backs up the asymmetric responses of investors to different investment possibilities. It looks at the evolution of financial technology (Fintech) and shows how it can be used to take the advantage of unique opportunities.

The study is different and novel from the previously published literature on this topic mainly because of its comprehensiveness, as it revolves around all industrial and commercial areas. The three main lines of research have been outlined, namely, classifying the many blockchain-based innovations that will alter the financial landscape in many industries; identifying whether these industries are a good fit for blockchain’s wealth creation potential; and directing researchers by outlining prospective study pathways.