The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach

Abstract:

This research sheds light on the major sins (fornication and sodomy) as two crimes that are prohibited by the divine laws, including its conclusion by the Islamic Sharia. Major sins so that life and the universe are organized, and the individual lives a life of chastity and purity free of filth and filth. The Islamic message included a set of preventive measures and remedial methods that, if the ummah were to apply them, they would live a decent life full of noble goals.

 We studied the changing of structural and optical properties of pure and Aluminum-doped ZnO thin films prepared by thermal evaporation technique on glass substrates at thickness (800±50)nm with changing of annealing temperatures ( 200,250,300 )℃ for one hour. The investigation of (XRD) indicates that the pure and doped ZnO thin films were polycrystalline of a hexagonal wurtzite structure with preferred orientation along (002) plane. The grain size was decreased with doping before annealing, but after annealing the grain size is increasing with the increase of annealing temperature for pure film whereas for the doped films with ratios 1 %, 2 % we found that the grain size is larger than that before annealing. The grain size

Electronic properties including (bond length, energy gap, HOMO, LUMO and density of state) as well as spectroscopic properties such like infrared, Raman scattering, force constant, reduced mass and longitu- dinal optical mode as a function of frequency are based on size and concentration of the molecular and nanostructures of aluminum nitride ALN, boron nitride BN and AlxB7-XN7 as nanotubes has calculated using Ab –initio approximation method dependent on density functional theory and generalized gradient approximation. The geometrical structure are calculated by using Gauss view 05 as a complementary program. Shows the energy gap of ALN, BN and AlxB7-XN7 as a function of the total number of atoms , start from smallest molecule to reached

The acrylic polymer composites in this study are made up of various weight ratios of cement or silica nanoparticles (1, 3, 5, and 10 wt%) using the casting method. The effects of doping ratio/type on mechanical, dielectric, thermal, and hydrophobic properties were investigated. Acrylic polymer composites containing 5 wt% cement or silica nanoparticles had the lowest abrasion wear rates and the highest shore-D hardness and impact strength. The increase in the inclusion of cement or silica nanoparticles enhanced surface roughness, water contact angle (WCA), and thermal insulation. Acrylic/cement composites demonstrated higher mechanical, electrical, and thermal insulation properties than acrylic/silica composites because of their lowe