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Publication
Using natural language processing for phishing detection
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Advisor(s)
Abstract(s)
We live in a world where computers are constantly changing
the way we do things. People spend many hours on their phones or
computers, whether it be for work or leisure purposes. The danger is
that these unsuspecting users can be targeted for attacks at any time
and can fall victim to many types of scams or phishing attacks. These
attacks can be harmful to the user by getting valuable credentials, money
or even installing malicious software on their devices, all while the user
is unaware of what has just happened. In a business environment these
can lead to mass data breeches which could end up costing a company
millions of euros. Many users are not trained to recognize phishing texts,
so an alternative solution is needed to help prevent users from falling
into these traps. In this paper we will be investigating Natural Language
Processing (NLP), a subsection of Machine Learning (ML) to try generate
solutions to the problem of phishing. We will investigating different
NLP solutions: Word2Vec, Doc2Vec and BERT, and different ML solutions:
RNN, LSTM, CNN and TD-IDF. All of these different approaches
provide good classification results ranging from f1-scores of 90.03–98.94.
Description
Keywords
Cybersecurity Machine learning Artificial intelligence Phishing Natural language processing
Pedagogical Context
Citation
Jonker, Richard Adolph Aires; Poudel, Roshan; Pedrosa, Tiago; Lopes, Rui Pedro (2021). Using natural language processing for phishing detection. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 540-552. ISBN 978-3-030-91884-2
Publisher
Springer Nature