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

Introduction. Hepatotoxicity is primarily-caused by oxidative stress and mitochondrial dysfunction; and, it is the principal factor that restricts the clinical efficacy of cyclophosphamide (Cpd), which is a chemotherapeutic drug that is frequently-used. The antioxidant capabilities have been demonstrated by dapagliflozin (Dapa), which is an inhibitor of sodium-glucose co-transporter-2 (SGLT2). Silymarin (Sil) is a chemical that is extracted from milk thistle. Researches have demonstrated that silymarin has hepatoprotective and antioxidant properties.

Aim.