Companies utilize competitive advantage as a tool to assist them gain more value for their products at a cheaper cost without sacrificing quality to provide greater features and services. Companies and services must use cybersecurity tools, training, and risk management strategies, as well as regularly upgrade systems as technology changes and evolves, to secure organizations, employees, and individuals. As a result, with the mediating function of cybersecurity, this study clarified the influence of strategic intelligence and asset management on boosting competitive advantage. A questionnaire was designed, and 300 questionnaires were collected out of 350 distributed to respondents working in Jordanian telecom companies. The study found a positive impact of both strategic intelligence and asset management on enhancing competitive advantage through the presence of the mediating role of cybersecurity.

In this digital age, fraudulent practices are among the most challenging that organizations must be aware of due to the increasing use of online transactions. This also applies to the banking sector whose business has become more complex with the recent developments in information and communication technology, which has changed the nature of bank fraud requiring advanced prevention measures. From this perspective, this paper aims to determine how cybersecurity affects fraud prevention for Jordanian commercial banks. A five-dimensional NIST cybersecurity framework was used. The research data was collected from 173 information technology managers in commercial banks listed on the Amman Stock Exchange. Structural equation modeling (SEM) was applied to investigate research hypotheses. The results of the research demonstrated the significant impact of cybersecurity in fraud prevention, especially detect function which had the largest impact among the dimensions of cybersecurity. Therefore, a set of recommendations were formulated for policymakers in Jordanian commercial banks, the most important of which is the adoption of multi-factor authentication (MFA) approaches for customer accounts, employee access, and biometric systems that add an additional layer of protection and make access to sensitive information to unauthorized individuals more difficult.

This study aims to analyze and examine the influence of internal auditor certification, risk assessment, and the role of senior management on the effectiveness of cybersecurity for internal auditors who have experience in cybersecurity and information technology. This research method is a quantitative method, data analysis uses structural equation modeling (SEM) with SmartPLS 3.0 software tools. The population of this study is internal auditors who have experience in cybersecurity and information technology. The sample for this study was 480 respondents who were determined by the snowball sampling method. The research data was obtained from an online questionnaire which was distributed via social media. The questionnaire was designed using a Likert scale of 1 to 5. The stages of data analysis were validity test, reliability test and significance test. The results of this study indicate that internal auditor certification has a positive effect on cybersecurity effectiveness, risk assessment has a positive effect on cybersecurity effectiveness, and the role of senior management has a positive effect on cybersecurity effectiveness.

In the era of IoT gaining traction, attacks on IoT-enabled devices are the order of the day that emanates the need for more protected IoT networks. IoT's key feature deals with massive amounts of data sensed by numerous heterogeneous IoT devices. Numerous machine learning techniques are used to collect data from different types of sensors on the objects and transform them into information relevant to the application. Furthermore, business and data analytics algorithms help in event prediction based on observed behavior and information. Routing information securely over the internet with limited resources in IoT applications is a key problem. The study proposes a model for detecting network anomalies in IoT devices to enhance the security of the devices. The study employed the IoT Botnet dataset, and K-fold cross-validation tests were used for validating the values of evaluation metrics. The average values of Accuracy, Precision, Recall, and F Score was 97.4.