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.

The study looks into the intersection of Artificial Intelligence (AI) with the Internet of Things (IoT), especially the legal, regulatory, and cybersecurity integration challenges within the context of UAE and Jordan's financial sectors. The objective of the study was to assess the relative impact of the cybersecurity challenges, legal infrastructures, and e-governance maturity on the cyber threats and trust of clientele. The study utilized a quantitative research design, gathering data through a survey distributed to employees and managers within a number of financial institutions. With a data sample of 400 employees, the survey data were analyzed through a variety of methods, such as descriptive statistics, reliability, Pearson correlations, and Structural Equation Modelling (SEM). The study established that the risks posed by inadequate cybersecurity infrastructures substantially increase the threats. Also, the risks posed by inadequate legal regulations and low e-governance maturity do not appear to increase the challenges. Legal adequacy positively impacts trust. Exposure to cyber threats with unmitigated risks and poor legal regulations and low e-governance maturity do not appear to increase the challenges. The study relies on the trust of cyber clientele to validate and uphold the proposed theoretical framework suggesting the need for an integrated approach consisting of high-quality legal regulations, comprehensive governance, and secure advanced cybersecurity to ensure the safe merging of AI and IoT. In addition, the study sheds light on the perspectives of policymakers, regulators, and financial institutions aiming to build safe and reliable digital financial systems in the UAE and Jordan.

Man-in-the-Browser (MitB) attacks represent a sophisticated class of web-based threats that manipulate browser functionality to intercept and modify user transactions in real-time. Traditional server-side detection mechanisms often fail to identify these attacks due to their client-side nature and encrypted communication channels. This paper presents a novel client-side runtime integrity agent that employs forensic monitoring and machine learning-based anomaly detection to identify MitB attacks at their source. The proposed system integrates DOM integrity verification, memory forensic analysis, and behavioral pattern recognition to detect malicious browser modifications before they can compromise user sessions. Our experimental evaluation demonstrates a detection accuracy of 97.3% with a false positive rate of 2.1%, significantly outperforming existing client-side detection methods. The system successfully identified various MitB attack vectors, including Zeus, SpyEye, and custom injection payloads, while maintaining a minimal computational overhead of less than 3% CPU utilization.

In the dynamic field of cybersecurity within intelligent vehicle systems, the sophistication of threats necessitates continual advancements in security technologies. Traditional Network Intrusion Detection Systems (NIDS) often fall short in detecting emerging and sophisticated intrusion methods, primarily due to their reliance on static datasets that fail to capture the nuanced dynamics and complexity of modern network intrusions. This study presents a sophisticated simulation for NIDS tailored to intelligent vehicle environments, utilizing the extensive capabilities of Scapy—a robust network manipulation tool—to generate a highly accurate dataset of network traffic reflective of real-world scenarios. We created a diverse dataset involving 100,000 network flows, covering a wide array of benign, malicious, and anomalous traffic patterns, to thoroughly evaluate the detection capabilities of our proposed system. This dataset was analyzed using a deep learning framework employing a Convolutional Neural Network (CNN), which demonstrated outstanding performance metrics: an accuracy of 99.08%, precision of 98.96%, recall of 99.11%, and an F1 score of 99.03%. These metrics showcase the system's enhanced capability to precisely classify various network flows, emphasizing the importance of realistic synthetic data in boosting the training and accuracy of NIDS in intelligent vehicles. The results of this research are significant, marking a step forward towards more flexible and preemptive security measures for intelligent vehicles, and effectively narrowing the gap between simulation-based testing and real-world network environments.

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.