Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

Modern civilization increasingly relies on sustainable and eco-friendly data centers as the core hubs of intelligent computing. However, these data centers, while vital, also face heightened vulnerability to hacking due to their role as the convergence points of numerous network connection nodes. Recognizing and addressing this vulnerability, particularly within the confines of green data centers, is a pressing concern. This paper proposes a novel approach to mitigate this threat by leveraging swarm intelligence techniques to detect prospective and hidden compromised devices within the data center environment. The core objective is to ensure sustainable intelligent computing through a colony strategy. The research primarily focusses on the

Mixed ligand complexes of bivalent metal ions, viz; Co(II), Ni(II), Cu(II) and Zn(II) of the composition [M(A)2((PBu3)2]in(1:2:2)(M:A:(PBu3). molar ratio, (where A- Anthranilate ion ,(PBu3)= tributylphosphine. M= Co(II),Ni(II),Cu(II) and Zn(II). The prepared complexes were characterized using flame atomic absorption, by FT-IR, UV/visible spectra methods as well as magnetic susceptibility and conductivity measurements. The metal complexes were tested in vitro against three types of pathogenic bacteria microorganisms: (Staphylococcus, Klebsiella SPP .and Bacillas)to assess their antimicrobial properties. Results. The study shows that all complexes have octahedral geometry; in addition, it has high activity against tested bacteria. Based on th

A field experiment was carried out in Horticulture Department / Collage of Agricultur e/University of Baghdad to study influence of adding ascorbic acid(asa) and bread yeast extract in snap bean cv.primel under irrigation with saline water using sodium chloride salt (NaCl) during spr ing season of 2016 .A factorial experiment using Randomized Complete Block Design( RCBD) with three replications wereconducted . The first factor includes three treatments of salinity which were tap water ( S0), 4ds.m-1(S1) and 8ds.m-1 (S2) . The second factor includes three treatments which were control treatment without any adding (C) ,ascorbic acid 0.3g.l-1( A ) and yeast extract 12g.l -1( Y ). Results showed significant and gradually decreases in all studie

A preventing shield for neutrons and gamma rays was designed using alternate layers of water and iron with pre-fixed dimensions in order to study the possibility of attenuating both neutrons and gamma-rays. ANISN CODE was prepared and adapted for the shield calculation using radiation doses calculation: Two groups of cross-section were used for each of neutrons and gamma-rays that rely on the one – dimensional transport equation using discrete ordinate's method, and through transforming cross-section values to values that are independent on the number of groups. The memory size required for the applied code was reduced and the results obtained were in agreement with those of standard acceptable document samples of cross –section, this a

rop simulation models play a pivotal role in evaluating irrigation management strategies to improve water use in agriculture. The aim of this study is to verify the validity of the Aquacrop model of maize under the surface and sprinkler irrigation systems, and a cultivation system, borders and furrows, and for two varieties of Maze (Fajr and Drakma) At two different sites in Iraq, Babylon and Al-Qadisiyah governorates. An experiment was conducted to evaluate the performance of the Aquacrop model in simulating canopy cover (CC), biomass (B), dry yield, harvest index (HI), and water productivity (WP). The results of RMSE, R2, MAE, d, NSE, CC, Pe indicated good results and high compatibility between measured and simulated values. The highest a

A nano-sensor for nitrotyrosine (NT) molecule was found by studying the interactions of NT molecule with new B24N24 nanocages. It was calculated using density functionals in this case. The predicted adsorption mechanisms included physical and chemical adsorption with the adsorption energy of −2.76 to −4.60 and −11.28 to −15.65 kcal mol−1, respectively. The findings show that an NT molecule greatly increases the electrical conductivity of a nanocage by creating electronic noise. Moreover, NT adsorption in the most stable complexes significantly affects the Fermi level and the work function. This means the B24N24 nanocage can detect NT as a Φ–type sensor. The recovery time was determined to be 0.3 s. The sensitivity of pure BN na