University of Twente Student Theses

Login

Improving the effectiveness of phishing detection Using lexical semantics; A machine-learning based approach

Rijnbergen, K.J. (2020) Improving the effectiveness of phishing detection Using lexical semantics; A machine-learning based approach.

[img] PDF
582kB
Abstract:Many share the opinion that phishing emails should automatically be detected, such that these emails can be filtered out and do not end up in our inbox. However, a method that perfectly does this has not yet been found. Prior research describes several methods that attempt to identify phishing emails based on structural properties, but to our knowledge, a better alternative does not yet exist. In this thesis, we propose a method that allows us to filter out these emails based on lexical semantics. We make use of machine learning-based algorithms in combination with a technique that carries the name of word embeddings, to design a method that can be used in automatic email classification. By implementing this method, we can let our computers automatically filter emails by making a judgement based on the contents of the emails, just like how they are presented to us as human beings.
Item Type:Essay (Bachelor)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:01 general works, 02 science and culture in general, 17 linguistics and theory of literature, 18 languages and literature, 30 exact sciences in general, 31 mathematics, 54 computer science, 70 social sciences in general
Programme:Industrial Engineering and Management BSc (56994)
Link to this item:http://purl.utwente.nl/essays/83384
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page