Abdulhameed N. Hameed

This work introduces a compact triple wideband Multiple-Input Multiple-Output (MIMO) antenna specifically designed for 5G applications. The antenna was designed simply by integrating three structures, each optimally nominated to operate within a specific frequency band. Subsequently, the antenna’s bandwidth was enhanced by incorporating a slot on the front side and an L-shaped structure on the rear side. The compact dimension of the antenna is about 37.5 × 37.5 × 1.6 mm ³ , corresponding to an electrical size of 0.375λ × 0.375λ × 0.016λ at 3 GHz. The antenna is fabricated and measured. Measurement results reveal that the developed antenna shows a fractional bandwidth of about 90.91% (5 GHz), 39.9% (6.6 GHz) and 20.4% (5 GHz) (Simulated: 101.7(40.6 GHz)) in the Sub-6 gigahertz, Ku and mm-Wave bands, respectively for |S11| < - 10dB. The proposed antenna achieves measured peak gains of 5 dBi and simulated gains of 5.5 dBi, and 10.1 dBi for the Sub-6, Ku, and mm-Wave bands, respectively for |S11| < -10dB. It has excellent diversity performance, with envelope correlation coefficients (ECC) of less than 0.06, 0.002 and 0.002 for Sub-6, Ku and mm-Wave bands respectively and diversity gains (DG) greater than 9.68, 9.99 and 9.99 for the Sub-6, Ku and mm-Wave bands respectively. Good TARC values are observed at 90-degree phase for all bands. In addition, the accepted simulated CCL values are observed for all bands. Also, the simulated and the measured MEG lies within −3 dB to −4.3 dB for all bands. Moreover, the radiation patterns in the H-plane of all bands are like omnidirectional patterns and in the E plane the antenna exhibits monopole like radiation patterns for the Sub 6 and Ku bands. The versatile multiband operation makes the designed antenna a reliable solution for advanced wireless communication systems.

Man-in-the-middle (MitM) attacks became one of the most risk attacks on OpenFlow communication channel in software-defined networking, its detection is a very hard task due there is no authentication in OpenFlow protocol. This channel is the most important in the network and is responsible for sending the control commands from the controller to the switches, so once the OpenFlow channel is hacked, the entire network is controlled by the attacker. Therefore, we propose a complementary solution to transport layer security protocol to detect man-in-the-middle attacks based on hybrid quantum-classical protocol. Based on the hybrid protocol, an easy-toimplement authentication between controller and switches depends on quantum and classical security layers. Also, detect eavesdropping on channel depending on quantum parameters. In this paper, we implement a simulation of hybrid protocol using a software-defined networking emulator for monitoring the OpenFlow channel to detect attacks, and the results showed the ease of detecting the eavesdrop and verifying the authentication of the other party with a hybrid method to get a high level of authentication.

The accuracy of the classification process always suffers from the high dimensionality problem due to the independent, irrelevant, redundant and not useful attributes of the dataset. In this research, feature selection techniques (wrapper selection method, and information gain method) are obtained to handle the mentioned problem by removing those features and reducing the dataset dimensions. The techniques include wrapper selection method and information gain method. This research predicates on the diabetes dataset in WEKA application, which contains checking seven models enforce wrapper selection method as an attribute evaluator, forwarding direction, backward, and bi-directional best-first search method and Naïve Bayes technique as a classifier method, checking eight models applying information gain method as attribute evaluator, as well as the ranker as a search method. Additional to demonstrate the decision tree and classification figures for the best-obtained models in each one technique. The results proved the ability of wrapper and information gain to choose a minimum number of features in order to classify the data with an accuracy of more than 76% in this work.