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Élément Dublin Core | Valeur | Langue |
---|---|---|
dc.contributor.author | Bedrane, Ilhem | - |
dc.contributor.author | Boumahdi, Faima ( Promotrice) | - |
dc.date.accessioned | 2022-12-13T12:26:58Z | - |
dc.date.available | 2022-12-13T12:26:58Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | https://di.univ-blida.dz/jspui/handle/123456789/20465 | - |
dc.description | ill., Bibliogr. Cote: ma-004-881 | fr_FR |
dc.description.abstract | In recent years, cyber criminals have successfully invaded many important information systems by using phishing mail, causing huge losses. The detection of phishing mail from big email data has been paid public attention. However, the camouflage technology of phishing mail is becoming more and more complex, and the existing detection methods are unable to confront with the increasingly complex deception methods and the growing number of emails. In this paper we transformed the probleme from classification of emails into similarity detection between two emails in order to classify them into 4 mains classes “ Normal, Harrassment , suspicious and fraudulent” to solve this probleme we used Siamese Neural networks which gave us an accuracy of 95.13%. Key words : NLP, Siamese network, deep learning, phishing attacks, phishing detection, similarity learning. | fr_FR |
dc.language.iso | en | fr_FR |
dc.publisher | Université Blida 1 | fr_FR |
dc.subject | NLP | fr_FR |
dc.subject | Siamese network | fr_FR |
dc.subject | deep learning | fr_FR |
dc.subject | phishing attacks | fr_FR |
dc.subject | phishing detection | fr_FR |
dc.subject | similarity learning | fr_FR |
dc.title | Detection of Phishing Emails | fr_FR |
dc.type | Thesis | fr_FR |
Collection(s) : | Mémoires de Master |
Fichier(s) constituant ce document :
Fichier | Description | Taille | Format | |
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Bedrane Ilhem.pdf | 1,97 MB | Adobe PDF | Voir/Ouvrir |
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