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

Water stress has a negative impact on the yield and growth of crops worldwide and consequently has a global impact on food security. Many biochemical changes occur in plants as a response to water stress, such as activation of antioxidant systems. Molybdenum (Mo) plays an important part in activating the expression of many enzymes, such as CAT, POD, and SOD, as well as increasing the proline content. Mo therefore supports the defence system in plants and plays an important role in the defence system of mung bean plants growing under water stress conditions. Four concentrations of Mo (0, 15, 30, and 45 mg·L−1) were applied to plants, using two approaches: (a) seed soaking and (b) foliar application. Mung bean plants were subject