Improving Phishing Detection with the Grey Wolf Optimizer
Conference object
Accepted version
Permanent lenke
https://hdl.handle.net/11250/3047807Utgivelsesdato
2022-04-11Metadata
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Originalversjon
https://doi.org/10.1109/ICEIC54506.2022.9748592Sammendrag
With the recent epidemic of COVID-19-themed scam and phishing, the efficient automated detection of such attacks is crucial. Although many anti-phishing solutions, such as lists and similarity and heuristic-based approaches detect attacks, methods still can be improved. Classification accuracy is highly dependent on the feature selection method used to select appropriate features for classification. In this article, a multi-objective grey wolf optimizer is used to select proper features for classifying phishing websites through a variational autoencoder. Our results indicate the superiority of the classification rate compared with related work: A classification rate of 97.49%, is obtained, thereby suggesting the feasibility of evaluating our work.