Small and medium-sized enterprises represent the majority of enterprises globally and yet have some difficulties in understanding the impact that cybersecurity threats could have on their businesses and the damage they could do to their assets. This study aims to measure the effectiveness of security practices at small-sized enterprises in Saudi Arabia in the event of a cybersecurity attack. Our paper is among the first research papers to measure the effectiveness of cybersecurity practices and the threat posed by cybersecurity breaches among small enterprises in the event of cybersecurity attacks. A total of 282 respondents participated, all of them representing small-sized enterprises in Saudi Arabia. The study applies multiple regression tests to analyze the effectiveness of 12 cybersecurity practices in three aspects: financial damage, loss of sensitive data, and restoration time, at small enterprises. The findings indicate that having an inspection team and a recovery plan may limit the financial damage caused by cybersecurity attacks on small enterprises. The results also show that cybersecurity awareness, knowledge of cybersecurity damage, and professionals’ salaries were related to the loss of sensitive data. Furthermore, the results indicate that contact with cybersecurity authorities and having an inspection team have statistically significant effects on restoration time.

Social engineering is one of the most inventive methods of gaining unauthorized access to information systems and obtaining sensitive information. This type of cybersecurity threat requires minimal technical knowledge because it relies on the organization’s human element. Social engineers use various techniques, such as phishing, to manipulate users into either granting them access to various systems or disclosing their private data and information. Social engineering attacks can cost organizations more than 100,000 USD per instance. Therefore, it is necessary for organizations to increase their users’ awareness of social engineering attacks to mitigate the problem. The aim of this study is to provide a measurement of social engineering awareness in the Saudi educational sector. To achieve the aim of this study, a questionnaire was developed and evaluated. A total of 465 respondents completed the survey and answered questions related to measuring their knowledge of social engineering. The results show that 34% of participants (158 participants) had previous knowledge of social engineering approaches. The results also indicate that there are significant differences between participants with prior knowledge of social engineering and those with no such knowledge in terms of their security practices and skills. The implication of this study is that training is an essential factor in increasing the awareness of social engineering attacks in the Saudi educational sector.

In the face of constantly changing cyber threats, a variety of actions, tools, and regulations must be considered to safeguard information assets and guarantee the confidentiality, reliability, and availability of digital resources. The purpose of this research is to create an artificial intelligence (AI)-driven system to enhance sustainability for cyber threat detection in Internet of Things (IoT) environments. This study proposes a modern technique named Artificial Fish Swarm-driven Weight-normalized Adaboost (AF-WAdaBoost) for optimizing accuracy and sustainability in identifying attacks, thus contributing to heightening security in IoT environments. CICIDS2017, NSL-KDD, and UNSW-NB15 were used in this study. Min-max normalization is employed to pre-process the obtained raw information. The proposed model AF-WAdaBoost dynamically adjusts classifiers, enhancing accuracy and resilience against evolving threats. Python is used for model implementation. The effectiveness of the suggested AF-WAdaBoost model in identifying different kinds of cyber-threats in IoT systems is examined through evaluation metrics like accuracy (98.69%), F-measure (94.86%), and precision (95.72%). The experimental results unequivocally demonstrate that the recommended model performed better than other traditional approaches, showing essential enhancements in accuracy and strength, particularly in a dynamic environment. Integrating AI-driven detection balances offers sustainability in cybersecurity, ensuring the confidentiality, reliability, and availability of information assets, and also helps in optimizing the accuracy of systems.

Adoption of Ambient Assisted Living (AAL) technologies for geriatric healthcare is suboptimal. This study aims to present the AAL Adoption Diamond Framework, encompassing a set of key enablers/barriers as factors, and describe our approach to developing this framework. A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. SCOPUS, IEEE Xplore, PubMed, ProQuest, Science Direct, ACM Digital Library, SpringerLink, Wiley Online Library and grey literature were searched. Thematic analysis was performed to identify factors reported or perceived to be important for adopting AAL technologies. Of 3717 studies initially retrieved, 109 were thoroughly screened and 52 met our inclusion criteria. Nineteen unique technology adoption factors were identified. The most common factor was privacy (50%) whereas data accuracy and affordability were the least common factors (4%). The highest number of factors found per a given study was eleven whereas the average number of factors across all studies included in our sample was four (mean = 3.9). We formed an AAL technology adoption framework based on the retrieved information and named it the AAL Adoption Diamond Framework. This holistic framework was formed by organising the identified technology adoption factors into four key dimensions: Human, Technology, Business, and Organisation. To conclude, the AAL Adoption Diamond Framework is holistic in term of recognizing key factors for the adoption of AAL technologies, and novel and unmatched in term of structuring them into four overarching themes or dimensions, bringing together the individual and the systemic factors evolving around the adoption of AAL technology. This framework is useful for stakeholders (e.g., decision-makers, healthcare providers, and caregivers) to adopt and implement AAL technologies.

The internet has been used by individuals, organizations, and governments for business, sports, health, banking, advertisement, education, and other services. Many websites have been developed and designed in the last several decades. However, most have not been developed and designed according to a shared set of design standards. Consequently, there is a need for an approach to evaluate the effectiveness of a website. A literature review was conducted to develop such an approach. Four experts were then consulted to inspect and evaluate the approach, and a questionnaire was completed by three categories: Internet users, website developers, and others to determine its final version. This research resulted in the development of an approach to evaluate website effectiveness, composed of three major criteria: design, content, and functionality, and 17 sub-criteria. The significance of this new approach is that it allows stakeholders to evaluate their websites and determine how to improve them in order to achieve their vision and mission.