Today,phishing is an online attack designed to obtain sensitive information such as credit card and bank account numbers,passwords,and usernames.We can find several anti-phishing solutions,such as heuristic detection,...Today,phishing is an online attack designed to obtain sensitive information such as credit card and bank account numbers,passwords,and usernames.We can find several anti-phishing solutions,such as heuristic detection,virtual similarity detection,black and white lists,and machine learning(ML).However,phishing attempts remain a problem,and establishing an effective anti-phishing strategy is a work in progress.Furthermore,while most antiphishing solutions achieve the highest levels of accuracy on a given dataset,their methods suffer from an increased number of false positives.These methods are ineffective against zero-hour attacks.Phishing sites with a high False Positive Rate(FPR)are considered genuine because they can cause people to lose a lot ofmoney by visiting them.Feature selection is critical when developing phishing detection strategies.Good feature selection helps improve accuracy;however,duplicate features can also increase noise in the dataset and reduce the accuracy of the algorithm.Therefore,a combination of filter-based feature selection methods is proposed to detect phishing attacks,including constant feature removal,duplicate feature removal,quasi-feature removal,correlated feature removal,mutual information extraction,and Analysis of Variance(ANOVA)testing.The technique has been tested with differentMachine Learning classifiers:Random Forest,Artificial Neural Network(ANN),Ada-Boost,Extreme Gradient Boosting(XGBoost),Logistic Regression,Decision Trees,Gradient Boosting Classifiers,Support Vector Machine(SVM),and two types of ensemble models,stacking and majority voting to gain A low false positive rate is achieved.Stacked ensemble classifiers(gradient boosting,randomforest,support vector machine)achieve 1.31%FPR and 98.17%accuracy on Dataset 1,2.81%FPR and Dataset 3 shows 2.81%FPR and 97.61%accuracy,while Dataset 2 shows 3.47%FPR and 96.47%accuracy.展开更多
A URL(Uniform Resource Locator)is used to locate a digital resource.With this URL,an attacker can perform a variety of attacks,which can lead to serious consequences for both individuals and organizations.Therefore,at...A URL(Uniform Resource Locator)is used to locate a digital resource.With this URL,an attacker can perform a variety of attacks,which can lead to serious consequences for both individuals and organizations.Therefore,attackers create malicious URLs to gain access to an organization’s systems or sensitive information.It is crucial to secure individuals and organizations against these malicious URLs.A combination of machine learning and deep learning was used to predict malicious URLs.This research contributes significantly to the field of cybersecurity by proposing a model that seamlessly integrates the accuracy of machine learning with the swiftness of deep learning.The strategic fusion of Random Forest(RF) and Multilayer Perceptron(MLP)with an accuracy of 81% represents a noteworthy advancement,offering a balanced solution for robust cybersecurity.This study found that by combining RF and MLP,an efficient model was developed with an accuracy of 81%and a training time of 33.78 s.展开更多
基金financially supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number(R.G.P.2/21/46)in part by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,under Grant KFU253116.
文摘Today,phishing is an online attack designed to obtain sensitive information such as credit card and bank account numbers,passwords,and usernames.We can find several anti-phishing solutions,such as heuristic detection,virtual similarity detection,black and white lists,and machine learning(ML).However,phishing attempts remain a problem,and establishing an effective anti-phishing strategy is a work in progress.Furthermore,while most antiphishing solutions achieve the highest levels of accuracy on a given dataset,their methods suffer from an increased number of false positives.These methods are ineffective against zero-hour attacks.Phishing sites with a high False Positive Rate(FPR)are considered genuine because they can cause people to lose a lot ofmoney by visiting them.Feature selection is critical when developing phishing detection strategies.Good feature selection helps improve accuracy;however,duplicate features can also increase noise in the dataset and reduce the accuracy of the algorithm.Therefore,a combination of filter-based feature selection methods is proposed to detect phishing attacks,including constant feature removal,duplicate feature removal,quasi-feature removal,correlated feature removal,mutual information extraction,and Analysis of Variance(ANOVA)testing.The technique has been tested with differentMachine Learning classifiers:Random Forest,Artificial Neural Network(ANN),Ada-Boost,Extreme Gradient Boosting(XGBoost),Logistic Regression,Decision Trees,Gradient Boosting Classifiers,Support Vector Machine(SVM),and two types of ensemble models,stacking and majority voting to gain A low false positive rate is achieved.Stacked ensemble classifiers(gradient boosting,randomforest,support vector machine)achieve 1.31%FPR and 98.17%accuracy on Dataset 1,2.81%FPR and Dataset 3 shows 2.81%FPR and 97.61%accuracy,while Dataset 2 shows 3.47%FPR and 96.47%accuracy.
文摘A URL(Uniform Resource Locator)is used to locate a digital resource.With this URL,an attacker can perform a variety of attacks,which can lead to serious consequences for both individuals and organizations.Therefore,attackers create malicious URLs to gain access to an organization’s systems or sensitive information.It is crucial to secure individuals and organizations against these malicious URLs.A combination of machine learning and deep learning was used to predict malicious URLs.This research contributes significantly to the field of cybersecurity by proposing a model that seamlessly integrates the accuracy of machine learning with the swiftness of deep learning.The strategic fusion of Random Forest(RF) and Multilayer Perceptron(MLP)with an accuracy of 81% represents a noteworthy advancement,offering a balanced solution for robust cybersecurity.This study found that by combining RF and MLP,an efficient model was developed with an accuracy of 81%and a training time of 33.78 s.
