The rapid expansion of the virtual world and the increasing integration of artificial intelligence (AI) have intensified cybersecurity threats, posing significant risks to national security, economic stability, and societal well-being. As digital transformation accelerates across sectors, cyber threats continue to grow in complexity and impact, often exceeding traditional security capabilities. This study analyzes the contemporary cybersecurity threat landscape globally and regionally, with a focus on Jordan and selected Arab countries, and examines the strategic importance of developing an integrated cybersecurity platform to address emerging virtual threats. Using a qualitative analytical approach, the study conducts a systematic review of peer-reviewed literature, international cybersecurity reports, and global indices (2015–2024). A thematic framework is applied to identify threat trends, institutional readiness gaps, and strategic enablers across governance, technology, human capacity, and cooperation dimensions. Findings indicate rising AI-driven cyber threats with notable economic, political, and social implications. Despite progress in several Arab countries, particularly GCC states, challenges remain in coordination, proactive risk management, and platform integration. The study proposes a conceptual cybersecurity platform integrating regulation, advanced technologies, human capital development, and international cooperation to enhance national and organizational resilience.
The rapid expansion of Internet of Things (IoT) devices has fundamentally reshaped traditional homes into highly interconnected smart environments, driving unprecedented convenience while simultaneously exposing critical cybersecurity vulnerabilities. Conventional centralized Intrusion Detection Systems (IDS) are increasingly inadequate for smart homes, owing to inherent privacy issues, latency, and susceptibility to single points of failure. Federated Learning (FL) offers a promising, privacy-preserving paradigm by enabling collaborative model training without the exchange of raw user data; however, FL remains vulnerable to model poisoning and integrity attacks. This comprehensive review systematically examines the integration of blockchain technology as a means to secure FL-based IDS in smart home contexts. We critically evaluate diverse blockchain frameworks, consensus protocols, and enhancement strategies that underpin the development of trustworthy, transparent, and resilient security architectures. Specifically, the review addresses two core research questions: (1) In what ways are blockchain frameworks and consensus mechanisms leveraged to secure FL-based IDS? and (2) What enhancement techniques and hybrid models are implemented to optimize the performance and efficiency of these integrated systems? Our synthesis reveals that permissioned blockchains—particularly those employing Proof of Authority (PoA) and Practical Byzantine Fault Tolerance (PBFT) consensus mechanisms—when combined with edge computing and lightweight architectural designs, deliver robust security while meeting the resource constraints characteristic of IoT devices. This technological synergy not only strengthens the resilience of the FL process but also advances the development of next-generation, secure smart home ecosystems.
The relentless growth in wireless traffic forces mobile operators to aggressively increase network capacity to meet subscriber demand. However, spectrum availability is not growing at the same rate as traffic; in fact spectrum resources are in markedly short supply, and, as a result, their cost remain high. The reuse of existing spectrum is a key point for network expansion through capacity densification. But this spectrum reused for network capacity expansion faces the problem of interference in general and external interference in particular which are most often very difficult to detect, identify and eliminate. In this article, we focus on the case of external interferences and on the basis of measurements and experiences obtained in the field, we propose methods and procedures to help identify external interferences and remove them. At the end of the paper a set of recommendations are proposed in other to reduce and cancel external interferences.
We deal with the problem of attenuation of electromagnetic waves due to precipitation. Indeed, the millimeter wave frequency (30–300 GHz) is a promising candidate for broadband wireless networks (4G and beyond), but atmospheric elements limit radio links to this frequency band. Precipitation is the important atmospheric element that causes the attenuation of a wave during its propagation and should therefore be estimated for the proper functioning of a link. It is with this in mind that our study focuses on the choice of an appropriate model for predicting the attenuation of a radio link after simulation of several models proposed in the literature in order to assess the impact of precipitation in the tropical region. The method used to achieve this objective consists firstly of analysing existing models and determining theoretical and actual unavailability using ITU-R methods and the Nomad monitoring tool; then secondly, we develop an application to evaluate the attenuation due to rain using the Da Silva, Mello and ITU-R models. The calculated attenuations were compared to experimental measurements, and Da Silva's model was found to be the best fit. On a local level, these results have a significant impact on the planning and optimization of communications infrastructures, enabling better performance of networks using millimeter waves. At the national level, this contributes to improved telecommunications networks and more reliable connectivity. Globally, this research can aid the development of high-frequency wireless communications in tropical regions, thereby promoting large-scale technological and economic advancement.
Artists illustrate objects to various degrees of complexity. As the amount of detail or the similarity to reality of a depiction decreases, the object tends to be reduced to its simplest, most relevant higher-level features (Harrison, 1981). One of the reasons Deep Neural Networks (DNN) may fail to identify objects in an image is that models are unable to recognize the order of importance of features such as shape, depth, or color within an image, which means even the most minute distortions of pixels within an image that would be imperceptible to humans would greatly impact the performance of the object detection models (Eykholt et al., 2018). However, by training DNN on artworks where the most prominent features defining specific objects are emphasized, perhaps a model can be made to be more resilient against small-scale changes in an image. In this paper, the correlation between the level of similarity to reality of images and artworks of an object and the accuracy of object detection models is investigated to test the ability of object detection models in identifying the most salient features of a particular object. The results of this report can help outline the efficacy of models only trained on real images in identifying increasingly abstract artworks that have simplified an object to its most prominent features. The experiment shows that the accuracies of models decrease as the images or illustrations provided become more abstract or simplified, which suggests the higher level features that identify a particular object are different in object detection models and humans.
