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

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

     Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust E

Most recent studies have focused on using modern intelligent techniques spatially, such as those
developed in the Intruder Detection Module (IDS). Such techniques have been built based on modern
artificial intelligence-based modules. Those modules act like a human brain. Thus, they should have had the
ability to learn and recognize what they had learned. The importance of developing such systems came after
the requests of customers and establishments to preserve their properties and avoid intruders’ damage. This
would be provided by an intelligent module that ensures the correct alarm. Thus, an interior visual intruder
detection module depending on Multi-Connect Architecture Associative Memory (MCA)

The aim of this study is to evaluate the levels of trace elements Magnesium (Mg), Zinc (Zn), Copper (Cu), and Selenium (Se) in blood sera of asthmatic patients by Atomic Absorption Spectrophotometry (AAS). The concentrations of Mg, Cu, and Zn have been determined by Flame Atomic Absorption spectrophotometry (FAAS), and Se with flameless Graphite Furnace Atomic Absorption Spectrophotometry (GFAAS). The study involves (55) asthmatic patients as study group and (28) subjects as control from both genders. Serum levels of Mg, Cu, and Se were significantly higher (p<0.001 for all) in patients when compared with healthy subjects, while Zn level was relatively significant (p<0.05). Our observations confirm the efficacy and applicability of (AAS) in

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Aim: The aim of this study is to determine the correlation between levels of certain seminal biochemical parameters and serum reproductive hormones, on the one hand, and sperm function tests, on the other hand, in asthenospermic patients. Patients and Methods: Sixty asthenospermic patients and twenty fertile men as a control group were included in this study. Semen samples were collected to perform seminal fluid analysis. Total protein, cholesterol, calcium, creatine kinase, and fructose were measured in the seminal plasma. Blood samples were collected for hormonal assay of serum reproductive hormones: testosterone, prolactin, luteinizing hormone, and follicle-stimulating hormone. Results: The results revealed a significant positive correla