Phishing Email Detection based on Named Entity Recognition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00330505" target="_blank" >RIV/68407700:21230/19:00330505 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21240/19:00330505 RIV/68407700:21730/19:00330505

Result on the web

<a href="http://www.scitepress.org/ProceedingsDetails.aspx?ID=2JXfLZNuB94=&t=1" target="_blank" >http://www.scitepress.org/ProceedingsDetails.aspx?ID=2JXfLZNuB94=&t=1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0007314202520256" target="_blank" >10.5220/0007314202520256</a>

Result language

angličtina

Original language name

Phishing Email Detection based on Named Entity Recognition

Original language description

This work evaluates two phishing detection algorithms, which are both based on named entity recognition (NER), on live traffic of Email.cz. The first algorithm was proposed in (Ramanathan and Wechsler, 2013). It is using NER and latent Dirichlet allocation (LDA) as feature extractors for random forest classifier. This algorithm achieved 100% F-measure on the publicly available testing dataset. We are using this algorithm as the baseline for our newly proposed solution. The newly proposed solution is using companies detected by the NER and it is comparing URLs present in the email content to the company URL profile (based on history). The company URL profile contains domains which are frequently mentioned in legitimate traffic from that domain. The advantage of the proposed solution is that it does not need phishing dataset, which is hard to get, especially for languages other than English. Our solution outperforms the baseline solution. Both solutions are able to detect previously und etected phishing attacks. Combination of the solutions achieves 100 % F-measure on the portion of live traffic.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 5th International Conference on Information Systems Security and Privacy

ISBN

978-989-758-359-9

ISSN

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e-ISSN

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Number of pages

5

Pages from-to

252-256

Publisher name

SciTePress

Place of publication

Madeira

Event location

Praha

Event date

Feb 23, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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