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By addressing the dearth of literature on the subject of cybersecurity risks and artificial intelligence (AI), this study aims to close a research gap by concentrating on the ever-changing environment of online social networks (OSNs) and technology. The main goals are to classify cyberattacks into categories like malware, phishing/spam and network intrusion detection; to identify efficient algorithms for preventing cyber threats; to review relevant literature from 2019 to 2020; and to use machine learning algorithms to detect suspicious behavior related to malware. The study offers a novel framework that suggests particular machine learning algorithms for every kind of cyber threat, hence improving cybersecurity knowledge and reaction capacities. This makes the research useful for examining the impact of cybersecurity on smart cities.

Thirty papers have been examined on AI and machine learning algorithms, including K-nearest-neighbor (KNN), convolutional neural networks (CNN) and Random Forest (RF), that were published in 2019 and 2020. Using analytical software (NVivo), a qualitative approach is used to retrieve pertinent data from the chosen research. The researchers divide cyberattacks into three groups: network intrusion detection, phishing/spam and malware.

The study’s conclusions center on how AI and machine learning algorithms linked to cybersecurity are reviewed in the literature, how cyberattacks are classified and how an inventive framework for identifying and reducing risks is proposed. This makes the research useful for researching the implications of cybersecurity for smart cities.

The practical implications of this research are noteworthy, particularly in the realms of technology, AI, machine learning and innovation. The utilization of the NVivo technique enhances decision-making in uncertain situations, making the study’s results more reliable. The findings showcase the applicability of tools in analyzing malicious cyberattacks to address issues related to social media attacks, emphasizing their practical utility. The study’s relevance is further highlighted by a real-world example, where a Kuwaiti public sector fell victim to a malware attack, underlining the importance of cybersecurity measures aligned with the New Kuwait 2035 strategic development plan. The innovative framework presented in the research guides the selection of algorithms for detecting specific malicious attacks, offering practical insights for securing information technology (IT) infrastructure in Kuwait.

The rapid digitization in Kuwait, accelerated by the COVID-19 pandemic, underscores the pivotal role of technology in government services. Ma’murov et al. (2023) emphasize the significance of digitization, particularly in accessing and verifying COVID-19 information. The call for a dedicated digital library for preserving pandemic-related material aligns with the evolving digital landscape. Cybersecurity emerges as a critical concern in Kuwait and the Gulf Cooperation Council (GCC), necessitating transnational cooperation (Nasser Alshabib and Tiago Martins, 2022). In the local context, the inefficiency of information security systems and low awareness among government employees pose cybersecurity challenges (Abdulkareem et al., 2014). Social media’s role during the pandemic highlights its significance, yet the need for cybersecurity in this domain remains underexplored (Ma’murov et al., 2023; Safi et al., 2023).

The unique aspect of the paper is its in-depth investigation of the relationship between cybersecurity and AI in OSNs. It uses a special application of machine learning methods, including CNN, RF and KNN, to identify suspicious behavior patterns linked to malware. The detailed analysis of 30 research papers released between 2019 and 2020, which informs the choice of suitable algorithms for diverse cyber threats, further emphasizes the study’s uniqueness. The novel framework that has been suggested categorizes assaults and suggests certain machine learning techniques for identification, offering a useful instrument to improve comprehension and reactions to a variety of cybersecurity issues.

This study investigated the factors contributing to the reduction of plagiarism. It focuses on elucidating the concept of plagiarism, its legal and ethical implications, the punitive measures implemented to address plagiarism and the level of awareness of plagiarism policies.

An online survey was administered to 649 students (160 male, 489 female) of the College of Social Sciences at Kuwait University (KU) to investigate the relationships between a number of factors linked to plagiarism, including its definition, legal and ethical implications, understanding of plagiarism regulations and the consequences associated with plagiarism.

The aforementioned factors and underlying causes that contribute to engaging in plagiarism were substantially correlated. Regression analysis revealed that variables such as legal and ethical considerations, familiarity with plagiarism regulations and the existence of sanctions were statistically significant predictors of the factors contributing to the occurrence of plagiarism.

The findings can help the Kuwait University administration and similar universities in the region focus on increasing students’ awareness of plagiarism and can aid in developing better policies that include the definition of plagiarism and penalties for undertaking this academic misconduct.

This study shows universities the importance of increasing students’ awareness of academic misconduct and can help create appropriate and robust policies.

This study aims to focus on how virtual campaigns are affecting voters in the elections of Kuwait, as well as whether such virtual campaigns will replace traditional campaigns in the post-COVID era.

This qualitative research adopts a purposeful sample when selecting participants from candidates and the managers of electoral campaigns in Kuwait. Fifteen participants were selected, which has been sufficient to achieve data saturation, and then, textual data were collected via semistructured interviews from 15 candidates and the managers of electoral campaigns in Kuwait during the COVID-19 pandemic.

The findings indicate that candidates preferred using virtual campaigns which enabled them to reach voters during the time of COVID-19’s lockdown. Majority of responses underlined that social media platforms do direct political messages to the voters. Hence, social media platforms should be perceived as preferred medium for communicating with supporters, especially in the post-COVID-19 era. However, some responses uphold the importance of keeping traditional political campaigns due to the peculiar nature of the Kuwaiti community where there is a need for socialization and meeting face-to-face with voters.

This research provides a new evaluation about the role of virtual political campaigns in Kuwait. It highlights the crucial and increasing role of virtual political campaigns in attracting voters; nevertheless, it found that virtual campaigns should be used as addendum to conventional political campaigns in the post-COVID-19 era in Kuwait.