Corporate crises are increasingly shaped by interconnected digital infrastructures, global supply chains, cloud services, artificial intelligence and data-intensive business models. This conceptual article examines how digital transformation is changing the character and management of corporate crises. It develops an integrated Digital Crisis Resilience Framework (DCRF). Drawing on crisis management, organisational resilience, dynamic capabilities, stakeholder and socio-technical systems theories, the article conceptualises resilience as a continuous strategic capability rather than a reactive emergency function. The DCRF integrates eight capabilities across the crisis lifecycle. Comparative analysis of the NotPetya/Maersk, SolarWinds, COVID-19, Colonial Pipeline and CrowdStrike cases illuminates the Digital Resilience Paradox: digitalisation can simultaneously strengthen adaptive capacity and increase systemic vulnerability. A five-level Digital Crisis Resilience Maturity Model, research propositions and an implementation roadmap are proposed. The article contributes by integrating fragmented research streams and extending crisis resilience from the firm level to the wider digital ecosystem. Read more

The essential need to protect digital records motivated this study, “Cybersecurity competencies required by business education lecturers for effective workplace digital records management". Two research questions guided the study, and two null hypotheses were tested by comparing how years of teaching experience and educational attainment influenced cybersecurity competencies of the lecturers. A descriptive survey research design was adopted. From a population of 57 business education lecturers, a census survey sampling was employed. A four-point response options questionnaire was used for data collection, and three experts validated it. The reliability of the instrument was established using Cronbach’s alpha, which yielded coefficients of 0.71 and 0.72. Mean and standard deviation were used to answer research questions and measure the spread in respondents' opinions, while one-way analysis of variance (ANOVA) was used to test the null hypotheses at a 0.05 level of significance. Findings revealed that foundational understanding of digital protection imperatives, cloud security architecture and implementation excellence, business impact evaluation and operational consequence analysis, artificial intelligence and machine learning security applications of cybersecurity competencies, among others, were highly required and that these cybersecurity competencies are highly beneficial to business education lecturers in public and private sectors for effective workplace digital records management. Based on the findings, it was concluded that those types of cybersecurity competencies were highly required and that cybersecurity competencies are highly beneficial for effective workplace digital records management. Therefore, among other things, it was recommended that business education lecturers should make personal sacrifices from their earnings and engage in cybersecurity competencies training to enable them to acquire very high knowledge and skills to perform on the job and sustain and remain relevant in their areas of operations. Read more

The rapid expansion of digital technologies has transformed organizational operations while simultaneously increasing exposure to sophisticated cybersecurity threats and cyber-enabled fraud. The convergence of remote work, cloud computing, artificial intelligence, and interconnected digital infrastructures has created new vulnerabilities that challenge traditional approaches to fraud prevention and cybersecurity management. Despite growing scholarly attention to these issues, existing research remains fragmented across multiple disciplines, limiting a comprehensive understanding of the factors influencing organizational resilience against cyber-enabled fraud. This study conducts a systematic literature review (SLR) to synthesize current knowledge on the relationship between emerging cybersecurity threats and organizational fraud risk. Following the PRISMA 2020 guidelines, relevant studies published between 2013 and 2025 were identified through searches of major academic databases, including Scopus, Web of Science, Google Scholar, ScienceDirect, IEEE Xplore, and SpringerLink. After applying predefined inclusion and exclusion criteria, 32 studies were selected for qualitative synthesis. The findings reveal four critical moderating factors that influence the extent to which cybersecurity threats translate into fraud outcomes: organizational cybersecurity culture, regulatory and compliance environments, remote work policies and technological infrastructure, and employee psychological well-being. The review demonstrates that technological controls alone are insufficient to mitigate cyber-enabled fraud. Instead, effective cybersecurity resilience emerges from the interaction of organizational, technological, regulatory, and human factors. Strong cybersecurity cultures, robust regulatory frameworks, secure digital infrastructures, and supportive workplace environments collectively reduce organizational vulnerability to cyber threats and fraudulent activities. The study contributes to the cybersecurity and fraud literature by providing an integrated conceptual framework that links previously fragmented research streams and offers practical insights for policymakers, organizational leaders, and cybersecurity professionals seeking to strengthen organizational resilience in an increasingly complex digital environment. Read more

This review critically evaluates the suitability of deep learning and explainable artificial intelligence approaches for intrusion detection in satellite ground-station environments, addressing the escalating cybersecurity risks facing the National Space Research and Development Agency (NASRDA) and broader space communication networks. Using a systematic narrative review across IEEE Xplore, ACM, Scopus and arXiv, the analysis compares CNN, LSTM, GRU, autoencoder and transformer-based IDS models, revealing that while reported accuracies frequently exceed 92% on benchmark datasets, performance declines by 20% to 35% under domain shift, demonstrating poor transferability to space–ground telemetry. XAI methods such as SHAP, LIME and Integrated Gradients appear in more than 80% of reviewed studies, yet empirical results show a 30% to 60% increase in inference latency, raising concerns about operational feasibility in real-time satellite control systems. A mathematical hybrid model combining CNN, LSTM and transformer components with a structured anomaly-scoring function and explanation regulariser is formulated to address these limitations. Findings indicate that multi-model fusion enhances anomaly sensitivity, domain-specific feature engineering improves robustness, and integrated XAI pathways strengthen analyst trust while exposing computational bottlenecks. The proposed conceptual architecture for NASRDA advances the field by aligning detection workflows, interpretability mechanisms and feedback loops with the constraints of aerospace communication systems. The review concludes by identifying key research priorities, including the development of satellite-specific datasets, real-traffic validation of hybrid IDS models, and deployment of low-latency XAI dashboards for operational security. Read more

This systematic review evaluates existing federated and deep learning intrusion detection approaches with a focus on their suitability for distributed satellite and critical infrastructure environments. Following PRISMA procedures, 13,213 records were identified, 11,265 duplicates removed, 1,948 screened, and 10 studies met full eligibility criteria. Across these studies, federated learning models achieved competitive detection outcomes, with reported accuracy ranging from eighty-eight percent to ninety-seven percent and F1 scores between eighty-five percent and ninety-six percent, often differing from centralised models by less than three percent. Communication efficiency improved substantially, with several studies demonstrating reductions of thirty to sixty percent in update bandwidth due to parameter rather than data transmission. Privacy preservation scored consistently high across all federated implementations, while centralised systems showed significant exposure risk. Weaknesses emerged in handling non-independent data, where performance dropped by up to ten percent in some studies, and in susceptibility to gradient poisoning, which degraded accuracy by seven percent in controlled experiments. Mathematical formulations remained underdeveloped, with limited convergence proofs and insufficient modelling of secure aggregation. The findings indicate that federated deep learning is methodologically superior for satellite data centres but requires realistic satellite traffic datasets, hybrid optimisation such as blockchain-assisted aggregation, and improved mathematical modelling to ensure robustness. Read more

Expectedly, International laws play an important role in cybersecurity and artificial intelligence (AI) by offering a framework to address new threats and regulate the development and application of AI technologies, especially with regard to potential cross-border cyberattacks. Imperatively, they ensure responsible behaviour by States and help to mitigate potential conflicts resulting from AI-powered cyber operations. However, because these technologies are developing so quickly, the extant laws that are supposed to address cyber threats arising from AI struggle to respond – partly because of the lack of flexibility of stakeholders. This paper is analytical in approach. It aims to examine international responses to AI-enabled cyber threats. In the end, it finds that the existing legal framework lacks robust provisions to address this growing threat. While the Malabo Convention lacks adequate domestication from the African jurisdictions, the Budapest Convention on Cybercrime and the EU GDPR do not have provisions on AI governance. To put this paper in a perspective, some recommendations have been made, such as the adoption of compensatory enforcement mechanisms, enhanced liability mechanisms, and the legislation of a robust legal framework among others. Read more

This systematic review explores methodologies for detecting and mitigating keyloggers, pervasive cybersecurity threats that surreptitiously capture keystrokes. After conducting thorough database searches, 26 relevant studies were found, showing a wide range of methods including machine learning algorithms, heuristic techniques, and behavior-based strategies. The review underscores the efficacy of combining proactive and reactive measures in countering keylogger threats, with machine learning algorithms exhibiting varying degrees of success. Significantly, creating interfaces that are easy for users to use is found to be a crucial element in improving user knowledge and making it easier to take quick action. However, the analysis also points out some drawbacks, such as the lack of extended verification for suggested approaches and variations in how algorithms are designed in different research. These findings underscore the imperative for ongoing innovation and collaboration among practitioners and policymakers to develop standardized protocols and address emerging threats comprehensively. Overall, this review offers valuable information on how to detect and prevent keyloggers, which can help direct future research in this important area. Read more

Denial-of-Service (DoS) attacks has been a critical challenge in cybersecurity, disrupting the availability of network services and causing significant operational and economic losses. To ascertain the most suitable approaches to mitigate to dilemma, this study compares the effectiveness of some selected machine learning models in identifying denial-of-service (DoS) attacks. Two ensemble learning models, Random Forest (RF) and Extreme Gradient Boosting (XGB), showed remarkable accuracy and dependability. RF performed almost perfectly on criteria including accuracy (99%), precision (99%), and recall (99%). Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) stood out as Deep Learning models, capturing complex patterns with CNN achieving an accuracy of 98% and a perfect AUC score of 1.00. The models utilized the Recursive Feature Elimination (RFE) to select significant features and ensured proper data balancing techniques for robust model training and evaluation, minimizing overfitting and enhancing generalization. The results highlight RF and CNN as the best-performing models, with RF offering interpretability and computational efficiency, while CNN excels in handling unstructured and complex datasets. This study underscores the need for context-driven model selection and suggests exploring hybrid approaches that integrate the strengths of ML and DL for improved DoS attack detection. Future work should aim to enhance scalability and adaptability for real-world cybersecurity applications. Read more

This study investigates the critical role of the human factor in enhancing organizational cybersecurity resilience, particularly within the context of tertiary institutions in Nigeria. As organizations increasingly depend on digital technologies, understanding how employee knowledge, behavior, and awareness impact information security is paramount. The research highlights the significant contribution of human error to security breaches, underscoring that even the most sophisticated technological defenses are vulnerable when individuals do not adhere to security protocols. Utilizing a comprehensive literature review, the study examines the implications of human behavior, including negligence and social engineering, on organizational security outcomes. Key findings indicate that insufficient training, lack of supervision, and poor understanding of security policies exacerbate vulnerabilities. Recommendations include implementing robust security awareness programs and ensuring that only qualified personnel teach keyboarding skills, as these practices can mitigate risks. Ultimately, the research advocates for a socio-technical approach to cybersecurity, emphasizing the need for collaboration between technical solutions and human factors to foster a more secure operational environment in the educational sector. Read more

Women and ethnic minorities have historically been underrepresented in the cybersecurity industry. Although there are an increasing number of open opportunities in the cybersecurity industry, this pattern has not changed. There is still an imbalance in the participation of women and ethnic minorities in the cybersecurity domain. Issues that impact both Africa and the global participation of women and ethnic minorities in cyber-security include inadequate representation and awareness as well as retention problems. Further, issues such as harassment, gender bias and the idea that cybersecurity is a “man’s world” are also contributing factors. This paper provides a framework for discussing some of the skills that are available to women and underrepresented groups and argues for the necessity to solve this problem. Programs striving to increase the participation of women and ethnic minorities in cybersecurity are discussed. Lastly, some suggestions to remediate this pervasive issue are also provided. Read more