Arab Academy for Science, Technology and Maritime Transport

The rapid digitization of financial transactions has increased efficiency but also exposed systems to sophisticated fraud attempts, posing significant challenges to ensuring transaction security. Traditional fraud detection approaches, including rule-based systems and conventional machine learning models, struggle to adapt to evolving fraud patterns, resulting in high false-positive rates and limited scalability. State-of-the-art methods, while leveraging deep learning, face limitations such as computational overhead, lack of transparency, and vulnerability to adversarial attacks. This study explores the integration of lightweight blockchain technology and deep learning for robust fraud detection in financial transactions. Lightweight blockchain ensures transaction immutability, transparency, and tamper-proof data sharing across nodes, addressing trust and security challenges. Meanwhile, deep learning provides dynamic and adaptive detection capabilities, employing neural networks to identify anomalous patterns in complex datasets. By reducing the computational and storage demands of traditional blockchain systems, the lightweight approach facilitates real-time fraud detection in resource-constrained environments, such as mobile and IoT devices. Our model combines these components, ensuring data integrity through blockchain while enabling efficient pattern recognition via deep learning, creating a system capable of addressing scalability, energy efficiency, and adaptability. Preliminary experiments demonstrate the model's effectiveness in reducing false positives, enhancing detection rates, and achieving scalability without compromising security or performance, marking a significant step toward secure and efficient financial ecosystems.

The goal of hybrid fiber-wireless (Fi-Wi) access networks is to ubiquitously integrate the accessible vast amounts of optical network bandwidth with wireless network mobility at minimal cost and simple network installation. Fi-Wi networks can employ both Radio over Fiber (RoF) and Radio and Fiber (R&F) technologies. The difference between them is that R&F uses two MAC controls: one for optical fiber and one for wireless networks. One can reduce interference within the wireless subnetwork while avoiding the negative effects of fiber optic propagation delays on the network. RoF, on the other hand, uses MAC control for both optical and wireless networks, resulting in network latency and congestion. This paper demonstrates (i) the use of semi-dynamic bandwidth allocation and (ii) the use of hierarchical frame aggregation for access in next-level integrated Ethernet passive optical network EPON/WLAN-based generations and presents the ongoing research to improve the Quality of Service in wireless and fiber access networks. The study includes an offered load up to 10 Gbps, reaching the up-to-date levels. The obtained results reveal that the data, voice, and video latency are reduced, respectively, by 35%, 25%, and 30%. At the same time, the corresponding throughput is increased, respectively, for data, voice, and video by 35%, 25%, and 30%.

This work presents a comprehensive investigation into the thermoelastic response of a porous micro-stretch elastic half-space medium subjected to the effect of rotation. The analysis is carried out within the frameworks of the Lord-Shulman (L-S) theory, the Dual-Phase-Lag (DPL) model, and Refined Dual-Phase-Lag (RDPL) extension. The novelty of this work lies in integrating rotational effects into the RDPL model for porous micro-stretch materials, deriving the fully coupled field equations using the Normal-Mode Analysis (NMA) technique, and offering a comprehensive comparison among the three thermoelastic theories. The inclusion of rotation introduces significant modifications to the thermo-mechanical fields due to Coriolis and centrifugal contributions, which are effectively captured by the refined phase-lag structure of the RDPL model. The results demonstrate that rotation strongly alters the temperature distribution, displacement field, micro-stretch response, and stress components. Specifically, the RDPL model predicts reduced displacement amplitudes, more damped thermal waves, and smoother stress distributions compared with the L-S and DPL theories, highlighting the influence of refined phase-lag parameters on wave attenuation and energy transport. Furthermore, rotation enhances the coupling between thermal and mechanical fields, leading to noticeable changes in wave speed, amplitude, and stability. These findings provide deeper insight into the dynamic behavior of rotating porous micro-structured materials and offer a more realistic framework for analyzing advanced thermoelastic systems.

Vehicular Ad Hoc Networks (VANETs) are wireless networks established between vehicles and their surrounding infrastructure, enabling the exchange of information. Consequently, many applications that can enhance passengers' safety and traffic flow are built upon this information. However, malicious nodes can manipulate the exchanged data to attack other nodes and disrupt the network's normal behavior. For example, if an attacker broadcasts a falsified location for a vehicle, the functionality of applications that rely on accurate location sharing will be compromised, potentially leading to deadly accidents. Although numerous Misbehavior Detection Schemes (MDSs) have been proposed to detect position falsification attacks, their effectiveness remains limited for certain attack types, raising concerns given the safety-critical nature of VANET applications. This paper proposes a machine learning-based method for detecting position falsification attacks. The proposed approach evaluates four machine-learning algorithms using three feature vectors (FV1, FV2, and FV3) composed of selected and derived features extracted from Basic Safety Messages (BSMs), in addition to a novel confidence-based Received Signal Strength Indicator feature, termed RSSIConf. The RSSIConf feature assesses the reliability of a sender's claimed position by comparing the measured RSSI with confidence intervals corresponding to the claimed sender-receiver distance. Experimental results show that the Random Forest classifier trained with FV2 features achieves the best overall performance, outperforming existing approaches with improvements ranging from 0.76% to 13.26% in accuracy and from 0.74% to 12.71% in F1-score across different position spoofing attack types. These improvements enhance the reliability of misbehavior detection and contribute to safer and more trustworthy VANET communications.

Alkali zinc lead fluorophosphate glasses doped with praseodymium oxide were fabricated via the standard melt-quenching technique. The glasses underwent comprehensive analysis through various spectroscopic techniques, including measurements of their absorption, reflectance, excitation, and emission spectra. Different physical and optical characteristics were assessed and deliberated upon, such as density, molar volume, polaron radius, optical bandgap, Urbach energy, extinction coefficient, refractive index, and Abbe number. Analysis of the optical absorption spectra showed nine bands attributable to the 4f-4f transitions of the Pr3+ ion. The Judd-Ofelt theory was used to assess the radiative properties of the Pr3+ ion. All glasses exhibited a uniform pattern for their Judd-Ofelt intensity parameters: Omega(4) > Omega(6) > Omega(2). The photoluminescence emission spectra exhibited eight distinct bands attributable to the P-3(0) -> F-3(4) (726 nm), P-3(1) -> F-3(4) (697 nm), P-3(0) -> F-3(2) (642 nm), P-3(0) -> H-3(6) (612 nm), P-3(0) -> H-3(5) (538 nm), P-3(1) -> H-3(5) (526 nm), P-3(2) -> H-3(5) (496 nm), and P-3(1) -> H-3(4) (476 nm) transitions. The chromaticity coordinates revealed a tunable luminescence, with the emission color progressively shifting from reddish orange to yellowish green. This was accompanied by an increase in correlated color temperature from 2032.73 to 6196.52 K.