Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

The melting duration in the photovoltaic/phase-change material (PV/PCM) system is a crucial parameter for thermal energy management such that its improvement can realize better energy management in respect to thermal storage capabilities, thermal conditions, and the lifespan of PV modules. An innovative and efficient technique for improving the melting duration is the inclusion of an exterior metal foam layer in the PV/PCM system. For detailed investigations of utilizing different metal foam configurations in terms of their convective heat transfer coefficients, the present paper proposes a newly developed mathematical model for the PV/PCM–metal foam assembly that can readily be implemented with a wide range of operating condition

A field experiment was conducted in Yusufiya sub-district - Mahmudiya township/Baghdad governorate in silty loam texture soil during the spring season of 2020. The experiment included three treatments with three replicates, as the Randomized Complete Block Design (RCBD) was used according to the arrangement of the split design block. The treatments are in the irrigation system, which included surface drip irrigation (T₁) and sprinkler irrigation (T₂). Secondly, the Irrigation levels including the irrigation using 0.70 Pan Evaporation Fraction PEF (I₁), irrigation using 1.00 PEF (I₂), and irrigation using 1.30 PEF (I₃). Coupled with, Pota

    In the drilling and production operations, the effectiveness of cementing jobs is crucial for efficient progress. The compressive strength of oil well cement is a key characteristic that reflects its ability to withstand forceful conditions over time. This study evaluates and improves the compressive strength and thickening time of Iraqi oil well cement class G from Babylon cement factory using two types of additives (Nano Alumina and Synthetic Fiber) to comply with the American Petroleum Institute (API) specifications. The additives were used in different proportions, and a set of samples was prepared under different conditions. Compressive strength and thickening time measurements were taken under different conditions. The amoun

Stereolithography (SLA) has become an essential photocuring 3D printing process for producing parts of complex shapes from photosensitive resin exposed to UV light. The selection of the best printing parameters for good accuracy and surface quality can be further complicated by the geometric complexity of the models. This work introduces multiobjective optimization of SLA printing of 3D dental bridges based on simple CAD objects. The effect of the best combination of a low-cost resin 3D printer’s machine parameter settings, namely normal exposure time, bottom exposure time and bottom layers for less dimensional deviation and surface roughness, was studied. A multiobjective optimization method was utilized, combining the Taguchi me

Two series of 1,3,4-oxadiazole derivatives at the sixth position of the 2,4-di-tert-butylphenol group were synthesized.