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Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

Computers, Materials & Continua 2024年第7期80卷 1325-1345页

作者： Nisreen Innab ahmed abdelgader fadol osman Mohammed Awad Mohammed Ataelfadiel Marwan Abu-Zanona Bassam Mohammad Elzaghmouri Farah H.Zawaideh Mouiad Fadeil Alawneh Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26110Jordan Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjloun26767Jordan

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based *** indicates a persistent rise in phishing ***,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to ***,it is imperative to utilize sophisticated algorithms to address this *** learning is a highly effective approach for identifying and uncovering these harmful *** learning(ML)approaches can identify common characteristics in most phishing *** this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing *** that,we used the normalization technique on the dataset to transform the range of all the features into the same *** findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best *** in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction ***,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

关键词： Social engineering attacks phishing attacks machine learning security artificial intelligence

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Boundedness Types of Perturbations on the Growth of Semigroups

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Advances in Pure Mathematics 2023年第1期13卷 1-10页

作者： Zuheir Khidir ahmed abdelgader Nariman Khider ahmed Abdalgadir Entisar Bakhit Bashir Elshikh Abualez Alamin ahmed Ali Department of Mathematics Faculty of Education Gezira University Wad Madani Sudan Department of Mathematics Faculty of Education Holy Quran University Wad Madani Sudan

We study types of boundedness of a semigroup on a Banach space in terms of the Cesáro-average and the behavior of the resolvent at the origin and also exhibit a characterization of type Hille-Yosida for the generators of ϕj-bounded strongly continuous semigroups. Furthermore, these results are used to investigate the effect of the Perturbation on the type of the growth of sequences.

关键词： Cesáro Average C0-Semigroups Boundedness Perturbation Stability Hille-Yosida Growth of Sequences

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Prevalence and Associated Factors of Back Pain among Medical Students in Sudan

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Health 2022年第9期14卷 931-938页

作者： Shahda Shms Eldeen Khlf Alla ahmed Ragda Kabashy Mohammed Bahar Lana Alsiddig Mohamed ahmed Aisha Ali Elameen Abbas Eiman Siddig Abd Elhadi ahmed Ruba Jabir Abdallah Seedahmed Shahd Yousif Hajomer abdelgader Arwa ahmed Alhussain ahmed Khalid Elfadil Ibrahim Husein Faculty of Medicine Omdurman Islamic University Omdurman Sudan Department of Orthopaedics and Traumatology Omdurman Islamic University Omdurman Sudan

Background: Back pain affects old and young adults, and it is a common complain among medical students. Objective: To estimate the prevalence of back pain and associated factors among medical students in Sudan. Method: An observational multi-universal cross-sectional study was conducted from December 2021 to February 2022, involving 433 medical students selected by simple random sampling. Data were collected by electronic Google Form questionnaire designed to assess demographic features, back pain characteristics, and triggering factors. Data analysis was done by statistical package for social sciences. Result: The prevalence of back pain among medical students in Sudan is 69.3%, with a statistically insignificant difference between preclinical and clinical students (p value: 0.378), and between male and female students (p value: 0.826). The lower back is the commonest site of pain (75%). The majority of the students experience moderate pain (53.9%). They did not use medications (81%). Sitting for long hours is the most common trigger (87.9%) and the pain did not interfere with the daily activities (71%). Conclusion: Back pain is a common complain among medical students in Sudan. Medical schools should raise the awareness of the students about back pain.

关键词： Back Pain Medical Students Medical School Sudan

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Boundedness Types of Perturbations on the Growth of Semigroups

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Advances in Pure Mathematics（理论数学进展） 2023年第1期

作者： Zuheir Khidir ahmed abdelgader Nariman Khider ahmed Abdalgadir Entisar Bakhit Bashir Elshikh Abualez Alamin ahmed Ali

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Prevalence and Associated Factors of Back Pain among Medical Students in Sudan

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Health（健康） 2022年第9期

作者： shahda shms eldeen khlf alla ahmed ragda kabashy mohammed bahar lana alsiddig mohamed ahmed aisha ali elameen abbas eiman siddig abd elhadi ahmed ruba jabir abdallah seedahmed shahd yousif hajomer abdelgader arwa ahmed alhussain ahmed khalid elfadil ibrahim husein

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