Matches in SemOpenAlex for { <https://semopenalex.org/work/W3092531413> ?p ?o ?g. }
- W3092531413 abstract "In the COVID-19 pandemic, people are enforced to adopt ‘work from home’ policy. The Internet has become an effective channel for social interactions nowadays. Peoples' immense dependence on digital platform opens doors for fraud. Phishing is a type of cybercrime to steal users' credentials from online platforms such as online banking, online business, e-commerce, online classroom, digital marketplaces, etc. Phishers develop fake webpages alike the original one and send spam emails to hook the users. Phishers seize users' credentials when an online user visits the counterfeit webpages through the spams. Researchers have introduced enormous tools like blacklist, white-list, and antivirus software to detect phishing webpages. Attackers always devise creative ways to exploit human and network weakness to penetrate cyber defense. This paper presents a data-driven framework for detecting phishing webpages using deep learning approach. More precisely, a multilayer perceptron, which is also referred as a feed-forward neural network is used to predict the phishing webpages. The dataset was collected from Kaggle and contains information of ten thousand webpages. It consists of ten attributes. The proposed model has achieved 95% training accuracy and 93% test accuracy." @default.
- W3092531413 created "2020-10-15" @default.
- W3092531413 creator A5000930895 @default.
- W3092531413 creator A5028430817 @default.
- W3092531413 creator A5043919282 @default.
- W3092531413 creator A5051878345 @default.
- W3092531413 creator A5071256439 @default.
- W3092531413 creator A5076219151 @default.
- W3092531413 date "2020-08-01" @default.
- W3092531413 modified "2023-09-30" @default.
- W3092531413 title "Phishing Attacks Detection using Deep Learning Approach" @default.
- W3092531413 cites W1530746640 @default.
- W3092531413 cites W1544402772 @default.
- W3092531413 cites W1964088283 @default.
- W3092531413 cites W1973249990 @default.
- W3092531413 cites W1990889672 @default.
- W3092531413 cites W2030744792 @default.
- W3092531413 cites W2033728501 @default.
- W3092531413 cites W2046603953 @default.
- W3092531413 cites W2089068202 @default.
- W3092531413 cites W2148614760 @default.
- W3092531413 cites W2158063174 @default.
- W3092531413 cites W2297844173 @default.
- W3092531413 cites W2318029646 @default.
- W3092531413 cites W2509152081 @default.
- W3092531413 cites W2593526178 @default.
- W3092531413 cites W2739066632 @default.
- W3092531413 cites W2782171552 @default.
- W3092531413 cites W2786104138 @default.
- W3092531413 cites W2791078573 @default.
- W3092531413 cites W2800391419 @default.
- W3092531413 cites W2890718808 @default.
- W3092531413 cites W2896959978 @default.
- W3092531413 cites W2921573932 @default.
- W3092531413 cites W2949344452 @default.
- W3092531413 cites W2950440169 @default.
- W3092531413 cites W2950616958 @default.
- W3092531413 cites W2972765591 @default.
- W3092531413 cites W2975388328 @default.
- W3092531413 cites W2977607695 @default.
- W3092531413 cites W2988892516 @default.
- W3092531413 cites W2989766846 @default.
- W3092531413 cites W2991210281 @default.
- W3092531413 cites W2994155906 @default.
- W3092531413 cites W2999300566 @default.
- W3092531413 cites W3004765419 @default.
- W3092531413 cites W3010992235 @default.
- W3092531413 cites W3011823135 @default.
- W3092531413 cites W3012375008 @default.
- W3092531413 cites W3012433340 @default.
- W3092531413 cites W3012643114 @default.
- W3092531413 cites W3012789274 @default.
- W3092531413 cites W3018772204 @default.
- W3092531413 cites W3018800955 @default.
- W3092531413 cites W3022944079 @default.
- W3092531413 cites W3023864986 @default.
- W3092531413 cites W3034857299 @default.
- W3092531413 cites W3045229347 @default.
- W3092531413 cites W4238922943 @default.
- W3092531413 doi "https://doi.org/10.1109/icssit48917.2020.9214132" @default.
- W3092531413 hasPublicationYear "2020" @default.
- W3092531413 type Work @default.
- W3092531413 sameAs 3092531413 @default.
- W3092531413 citedByCount "24" @default.
- W3092531413 countsByYear W30925314132020 @default.
- W3092531413 countsByYear W30925314132021 @default.
- W3092531413 countsByYear W30925314132022 @default.
- W3092531413 countsByYear W30925314132023 @default.
- W3092531413 crossrefType "proceedings-article" @default.
- W3092531413 hasAuthorship W3092531413A5000930895 @default.
- W3092531413 hasAuthorship W3092531413A5028430817 @default.
- W3092531413 hasAuthorship W3092531413A5043919282 @default.
- W3092531413 hasAuthorship W3092531413A5051878345 @default.
- W3092531413 hasAuthorship W3092531413A5071256439 @default.
- W3092531413 hasAuthorship W3092531413A5076219151 @default.
- W3092531413 hasConcept C108827166 @default.
- W3092531413 hasConcept C110875604 @default.
- W3092531413 hasConcept C136764020 @default.
- W3092531413 hasConcept C154945302 @default.
- W3092531413 hasConcept C165696696 @default.
- W3092531413 hasConcept C21959979 @default.
- W3092531413 hasConcept C22735295 @default.
- W3092531413 hasConcept C2779797433 @default.
- W3092531413 hasConcept C2781345505 @default.
- W3092531413 hasConcept C38652104 @default.
- W3092531413 hasConcept C41008148 @default.
- W3092531413 hasConcept C541664917 @default.
- W3092531413 hasConcept C83860907 @default.
- W3092531413 hasConceptScore W3092531413C108827166 @default.
- W3092531413 hasConceptScore W3092531413C110875604 @default.
- W3092531413 hasConceptScore W3092531413C136764020 @default.
- W3092531413 hasConceptScore W3092531413C154945302 @default.
- W3092531413 hasConceptScore W3092531413C165696696 @default.
- W3092531413 hasConceptScore W3092531413C21959979 @default.
- W3092531413 hasConceptScore W3092531413C22735295 @default.
- W3092531413 hasConceptScore W3092531413C2779797433 @default.
- W3092531413 hasConceptScore W3092531413C2781345505 @default.
- W3092531413 hasConceptScore W3092531413C38652104 @default.
- W3092531413 hasConceptScore W3092531413C41008148 @default.
- W3092531413 hasConceptScore W3092531413C541664917 @default.