The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat

Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over

Obesity is a common disease that resulted from over-nutrition in adults and children. It rarely causes damage to the centers of food in the brain. Obesity is defined as an increased body weight from its natural limit which is resulted from the accumulation of excessive amounts of fatty tissue incredibly up to 20% in males, 30 % in females unless this increase is not due to an increase in muscles as in athletes or accumulation of water in the body which is resulted from Mesothelioma or the magnitude of the skeleton.Obesity is the increase of the total average of fat in the body compared to other tissues, which causes an increasing body weight, thereby increasing body mass. The fatty child has an increase in the stored fatty layer under th

The human being is a social being and human communication is a fundamental axis in the process of social interaction, and the characteristic of social shyness grows through the interaction of the individual with his environment. Identifying the level of shyness among university students, and identifying the differences in shyness among university students according to specialization (scientific, humanities). In order to achieve the objectives of the research, the researcher prepared a measure of shyness based on Zimbardo's theory, and the components that he identified are the (behavioral, cognitive, and emotional components). The validity and reliability were calculated using the Cronbach's alpha method, reaching (0.94), and the scale in

The research aims to identify the level of psychological pollution in Iraqi society and the significant differences in psychological pollution in the Iraqi society according to variables. A sample of (600) individuals randomly selected from all governorates of Iraq, with (285) males and (315) females was used in the current study. The researcher adopted the scale of (Muhammad, 2004) psychological pollution consisting of (118) items, which limited (46) items after modification distributed into four areas: denial and abuse of the civilized identity, attachment to foreign formal aspects, effeminacy, and anarchism. The results of the research showed that there is no statistical significance among the individuals of the research sample. They

Complexes of some metal ions ( Mn(I? ) , Co(??) , Ni(??) ,Cu (??) , Zn(I?) , Cd (??) , and Hg(??) ) with 8-hydroxyquinoline (Oxine) and 2- Picoline (2-pic ) have been synthesized and characterized on the basis of their FT-IR. and Uv-visible spectroscopy ,atomic absorption molar conductivity measurements and magnetic susceptibility ,from the results obtained the following general formula has been given for prepared complexes [M (oxine)2 (2-pic)2]where M = M(??) = Mn , Co , Ni , Cu , Zn , Cd , Hg(oxine)- = ionic ligand 8-hydroxyquinolin (oxinato)(2- pic) = 2- picoline

Investigations made and soil samples brought from 14 sites in different areas, including Abu Ghraib and Al Anbar. Tests and measurements made in the Microbiology Laboratory at the College of Agriculture, University of Anbar. Department of Soil Sciences and water Resources, in order to isolate bio-fertilizers and test isolates fixing nitrogen in atmosphere and solvents for phosphorous compounds efficiency. The experiment included isolating and diagnosis of bacteria from rhizosphere soils of different plants that were brought from different agricultural areas, 74 isolates obtained by soils alleviation, and then the bio-chemical morphological and microscopic characteristics of these soils studied. The results showed that the most abundant and

To evaluate the Interaction of Mn(II), Fe(II), Co(II), Ni(II),Cu(II), Zn(II) And Cd(II) Mixed- Ligand Complexes of cephalexin mono hydrate (antibiotics) And Furan-2-Carboxylic Acid To The Different DNA Sources. All the metal complexes were observed to cleave the DNA. A difference in the bands of complexes .The cleavage efficiency of the complexes compared with that of the control is due to their efficient DNA-binding ability and the other factors like solubility and bond length between the metal and ligand may also increase the DNA-binding ability. The ligands (Cephalexin mono hydrate (antibiotics) and Furan-2- Carboxylic acid and there newly synthesized metal complexes shows good antimicrobial activities and Binding DNA , thus, can be used

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