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Machine Learning Techniques for Detecting phishing url Attacks

Computers, Materials & Continua 2023年第4期75卷 1271-1290页

作者： Diana T.Mosa Mahmoud Y.Shams Amr AAbohany El-Sayed M.El-kenawy M.Thabet Department of Cyber Security College of Engineering and Information TechnologyBuraydah Private CollegesBuraydah51418Kingdom of Saudi Arabia Faculty of Computers and Information Kafrelsheikh UniversityKafrelsheikh33516Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33516Egypt Delta Higher Institute of Engineering and Technology Mansoura35111Egypt Faculty of Computers and Information Fayoum UniversityFayoumEgypt

Cyber Attacks are critical and destructive to all industry *** affect social engineering by allowing unapproved access to a Personal Computer(PC)that breaks the corrupted system and threatens *** defense of security requires understanding the nature of Cyber Attacks,so prevention becomes easy and accurate by acquiring sufficient knowledge about various features of Cyber ***-Security proposes appropriate actions that can handle and block attacks.A phishing attack is one of the cybercrimes in which users follow a link to illegal websites that will persuade them to divulge their private *** of the online security challenges is the enormous number of daily transactions done via phishing *** Cyber-Security have a priority for all organizations,Cyber-Security risks are considered part of an organization’s risk management *** paper presents a survey of different modern machine-learning approaches that handle phishing problems and detect with high-quality accuracy different phishing attacks.A dataset consisting of more than 11000 websites from the Kaggle dataset was utilized and studying the effect of 30 website features and the resulting class label indicating whether or not it is a phishing website(1 or−1).Furthermore,we determined the confusion matrices of Machine Learning models:Neural Networks(NN),Na飗e Bayes,and Adaboost,and the results indicated that the accuracies achieved were 90.23%,92.97%,and 95.43%,respectively.

关键词： Cyber security phishing attack url phishing online social networks machine learning

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