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Named Entity Recognition and Its Application to Phishing Detection
Pop, Tomáš ; Skopal, Tomáš (advisor) ; Vomlelová, Marta (referee)
This thesis focuses on named entity recognition applied to email phishing detection. Named entity recognition is a classification task that aims to extract information from a text into a predefined set of categories (named entities), such as organizations, person names, or locations. The thesis describes various named entity recognition approaches, ranging from simple utilizations of neural networks to the current state-of-the-art archi- tectures. The most prevalent libraries and their models in named entity recognition are compared against each other from the computational and predictive performance per- spective on the publicly available Enron email dataset. Moreover, differences in terms of named entities between positive (including phishing) and negative emails are measured on a proprietary dataset. Ultimately, the proprietary dataset is used for an experiment where a phishing email classification workflow is enriched with named entities to conclude whether named entities are helpful for the classifier to improve predictive performance. According to the experiment outcomes, a noticeable dissimilarity was measured regarding named entities in positive and negative emails. However, in the phishing email classifica- tion experiment with the provided dataset, it was concluded that named entities do not offer...
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