Matches in SemOpenAlex for { <https://semopenalex.org/work/W3165193011> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W3165193011 endingPage "656" @default.
- W3165193011 startingPage "650" @default.
- W3165193011 abstract "Phishing is a cybercrime technique in which the attacker creates a copy of genuine websites with the same color pattern, layout, font, and logo and with a domain name that matches with the real one. Then, broadcast this fake website through various online modes like emails and social media. The attacker creates lucrative offers or discounts to lure in people to click on the phishing link. Once the user clicks on this phishing link, they a re directed to the duplicate website that the attacker had created. The user believes that it is the real website and enters his/her login details and other confidential data. This data is stored on the attacker’s server thus giving him full access to the victim’s data. The phishing attack is mainly targeted to collect confidential data of the victim. This data includes Username, Passwords, Bank details, security Credit card numbers etc. Machine Learning algorithms are being used widely in detecting phishing websites. This paper shows performance analysis of three Machine learning algorithms used for URL phishing detection. These algorithms are Extreme Learning Machine, Support Vector Machine and Naïve Bayes algorithm. The paper analyses these algorithms on the parameters of Accuracy, Precision, Recall, F1 score and Confusion matrix. The dataset includes 11,000 entries and 30 features from UC Irvine dataset repository. The literature survey shows how only importance is given to only one parameter i.e., Accuracy parameter when analyzing performance of the URL phishing detection algorithms. This paper concludes on how Accuracy parameter does not show full picture on the overall performance of the URL phishing detection algorithms and also how Precision and Recall parameters are very important in understanding the working of these algorithms." @default.
- W3165193011 created "2021-06-07" @default.
- W3165193011 creator A5018001614 @default.
- W3165193011 creator A5054802397 @default.
- W3165193011 date "2021-05-26" @default.
- W3165193011 modified "2023-09-30" @default.
- W3165193011 title "Performance Analysis of Machine Learning Algorithms Used for Web Based Phishing Detection" @default.
- W3165193011 cites W1894532194 @default.
- W3165193011 cites W2889851289 @default.
- W3165193011 cites W2892847381 @default.
- W3165193011 doi "https://doi.org/10.51201/jusst/21/05187" @default.
- W3165193011 hasPublicationYear "2021" @default.
- W3165193011 type Work @default.
- W3165193011 sameAs 3165193011 @default.
- W3165193011 citedByCount "0" @default.
- W3165193011 crossrefType "journal-article" @default.
- W3165193011 hasAuthorship W3165193011A5018001614 @default.
- W3165193011 hasAuthorship W3165193011A5054802397 @default.
- W3165193011 hasBestOaLocation W31651930111 @default.
- W3165193011 hasConcept C109297577 @default.
- W3165193011 hasConcept C110875604 @default.
- W3165193011 hasConcept C113324615 @default.
- W3165193011 hasConcept C11413529 @default.
- W3165193011 hasConcept C119857082 @default.
- W3165193011 hasConcept C12267149 @default.
- W3165193011 hasConcept C136764020 @default.
- W3165193011 hasConcept C138602881 @default.
- W3165193011 hasConcept C145097563 @default.
- W3165193011 hasConcept C154945302 @default.
- W3165193011 hasConcept C2983355114 @default.
- W3165193011 hasConcept C38652104 @default.
- W3165193011 hasConcept C41008148 @default.
- W3165193011 hasConcept C52001869 @default.
- W3165193011 hasConcept C83860907 @default.
- W3165193011 hasConceptScore W3165193011C109297577 @default.
- W3165193011 hasConceptScore W3165193011C110875604 @default.
- W3165193011 hasConceptScore W3165193011C113324615 @default.
- W3165193011 hasConceptScore W3165193011C11413529 @default.
- W3165193011 hasConceptScore W3165193011C119857082 @default.
- W3165193011 hasConceptScore W3165193011C12267149 @default.
- W3165193011 hasConceptScore W3165193011C136764020 @default.
- W3165193011 hasConceptScore W3165193011C138602881 @default.
- W3165193011 hasConceptScore W3165193011C145097563 @default.
- W3165193011 hasConceptScore W3165193011C154945302 @default.
- W3165193011 hasConceptScore W3165193011C2983355114 @default.
- W3165193011 hasConceptScore W3165193011C38652104 @default.
- W3165193011 hasConceptScore W3165193011C41008148 @default.
- W3165193011 hasConceptScore W3165193011C52001869 @default.
- W3165193011 hasConceptScore W3165193011C83860907 @default.
- W3165193011 hasIssue "05" @default.
- W3165193011 hasLocation W31651930111 @default.
- W3165193011 hasOpenAccess W3165193011 @default.
- W3165193011 hasPrimaryLocation W31651930111 @default.
- W3165193011 hasRelatedWork W1763839923 @default.
- W3165193011 hasRelatedWork W2026251092 @default.
- W3165193011 hasRelatedWork W2114635788 @default.
- W3165193011 hasRelatedWork W2783049111 @default.
- W3165193011 hasRelatedWork W2983506648 @default.
- W3165193011 hasRelatedWork W3034738955 @default.
- W3165193011 hasRelatedWork W3165193011 @default.
- W3165193011 hasRelatedWork W4205958290 @default.
- W3165193011 hasRelatedWork W4291802110 @default.
- W3165193011 hasRelatedWork W2588870966 @default.
- W3165193011 hasVolume "23" @default.
- W3165193011 isParatext "false" @default.
- W3165193011 isRetracted "false" @default.
- W3165193011 magId "3165193011" @default.
- W3165193011 workType "article" @default.