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

Halgas, Lukas, Agrafiotis, Ioannis, Nurse, Jason R. C. (2020) Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs). In: Lecture Notes in Computer Science. Information Security Applications: 20th International Conference, WISA 2019, Jeju Island, South Korea, August 21–24, 2019, Revised Selected Papers. 11897. Springer ISBN 978-3-030-39302-1. (doi:10.1007/978-3-030-39303-8_17) (KAR id:75746)

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https://doi.org/10.1007/978-3-030-39303-8_17

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.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1007/978-3-030-39303-8_17
Subjects: Q Science > QA Mathematics (inc Computing science)
T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Jason Nurse
Date Deposited: 09 Aug 2019 21:27 UTC
Last Modified: 16 Feb 2021 14:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/75746 (The current URI for this page, for reference purposes)
Nurse, Jason R. C.: https://orcid.org/0000-0003-4118-1680
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