About: Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #75746 Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)
sameAs	Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)
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http://eprints.org/ontology/hasDocument	Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs) (PDF) Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs) (Other) Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs) (Other) Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs) (Other) Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs) (Other) Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs) (Other)
dc:hasVersion	Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs) (PDF)
Title	Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2020-01-25
Creator	Ioannis Agrafiotis Lukas Halgas Jason R. C. Nurse
status	peer reviewed published
Publisher	Springer
abstract	The emergence of online services in our daily lives has been accompanied by a range of malicious attempts to trick individuals into performing undesired actions, often to the benefit of the adversary. The most popular medium of these attempts is phishing attacks, particularly through emails and websites. In order to defend against such attacks, there is an urgent need for automated mechanisms to identify this malevolent content before it reaches users. Machine learning techniques have gradually become the standard for such classification problems. However, identifying common measurable features of phishing content (e.g., in emails) is notoriously difficult. To address this problem, we engage in a novel study into a phishing content classifier based on a recurrent neural network (RNN), which identifies such features without human input. At this stage, we scope our research to emails, but our approach can be extended to apply to websites. Our results show that the proposed system outperforms state-of-the-art tools. Furthermore, our classifier is efficient and takes into account only the text and, in particular, the textual structure of the email. Since these features are rarely considered in email classification, we argue that our classifier can complement existing classifiers with high information gain.
Is Part Of	Financial Cryptography and Data Security https://kar.kent.ac.uk/id/repository
Subject	QA Mathematics (inc Computing science) T Technology
list of authors	https://kar.kent.ac.uk/id/eprint/75746#authors
presented at	20th World Conference on Information Security Applications (WISA 2019)
volume	11897
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #75746 Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)

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