Título:	From feature engineering and topics models to enhanced prediction rates in phishing detection
Autor(es):	Gualberto, Éder Souza Souza Júnior, Rafael Timóteo de Vieira, Thiago Pereira de Brito Costa, João Paulo Carvalho Lustosa da Duque, Cláudio Gottschalg
ORCID:	https://orcid.org/ 0000-0002-2917-3605 https://orcid.org/ 0000-0003-1101-3029 https://orcid.org/ 0000-0003-0512-374X https://orcid.org/ 0000-0002-8616-4924 https://orcid.org/ 0000-0003-3558-466X
Assunto:	Crime por computador Extração de recursos Aprendizado do computador Inteligência artificial Processamento de linguagem natural (Computação) Correio eletrônico
Data de publicação:	21-Abr-2021
Editora:	IEEE
Referência:	GUALBERTO, Eder S. et al. From feature engineering and topics models to enhanced prediction rates in phishing detection. IEEE Access, v. 8, p. 76368-76385, 2021. DOI: 10.1109/ACCESS.2020.2989126. Disponível em: https://ieeexplore.ieee.org/abstract/document/9075252. Acesso em: 13 out. 2021.
Abstract:	Phishing is a type of fraud attempt in which the attacker, usually by e-mail, pretends to be a trusted person or entity in order to obtain sensitive information from a target. Most recent phishing detection researches have focused on obtaining highly distinctive features from the metadata and text of these e-mails. The obtained attributes are then used to feed classification algorithms in order to determine whether they are phishing or legitimate messages. In this paper, it is proposed an approach based on machine learning to detect phishing e-mail attacks. The methods that compose this approach are performed through a feature engineering process based on natural language processing, lemmatization, topics modeling, improved learning techniques for resampling and cross-validation, and hyperparameters configuration. The first proposed method uses all the features obtained from the Document-Term Matrix (DTM) in the classification algorithms. The second one uses Latent Dirichlet Allocation (LDA) as a operation to deal with the problems of the “curse of dimensionality”, the sparsity, and the text context portion included in the obtained representation. The proposed approach reached marks with an F1-measure of 99.95% success rate using the XGBoost algorithm. It outperforms state-of-the-art phishing detection researches for an accredited data set, in applications based only on the body of the e-mails, without using other e-mail features such as its header, IP information or number of links in the text.
Licença:	This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
DOI:	10.1109/ACCESS.2020.2989126
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