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Automatic recognition of online scam websites

Carlo Zoccoli

Automatic recognition of online scam websites.

Rel. Paolo Garza. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2021


The case study is the classification of fraudulent and legit websites based on text mining technique. Several attempts are done by the candidate with the purpose to maximize the integrity of the classifier. The case-study is divided in shallow and deep learning approaches for the classification task. In the shallow learning approach, the term's frequency, Cialdini's principles and sentiment analysis features are extracted from the corpus text and are submitted to the classifiers. A different approach is the using the spam email dataset as training set such that it is possible to find a relationship between scam and spam case. In deep learning approach, email dataset is used for the training phase and the websites' dataset is used for the transfer learning technique, on neural networks architecture with different layers.

Relators: Paolo Garza
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 86
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Aziende collaboratrici: ERMES CYBER SECURITY S.R.L.
URI: http://webthesis.biblio.polito.it/id/eprint/19182
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