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Towards a Multi-Layered Phishing Detection.

Rendall, K., Nisioti, A. and Mylonas, A., 2020. Towards a Multi-Layered Phishing Detection. Sensors, 20 (16), 4540.

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DOI: 10.3390/s20164540

Abstract

Phishing is one of the most common threats that users face while browsing the web. In the current threat landscape, a targeted phishing attack (i.e., spear phishing) often constitutes the first action of a threat actor during an intrusion campaign. To tackle this threat, many data-driven approaches have been proposed, which mostly rely on the use of supervised machine learning under a single-layer approach. However, such approaches are resource-demanding and, thus, their deployment in production environments is infeasible. Moreover, most previous works utilise a feature set that can be easily tampered with by adversaries. In this paper, we investigate the use of a multi-layered detection framework in which a potential phishing domain is classified multiple times by models using different feature sets. In our work, an additional classification takes place only when the initial one scores below a predefined confidence level, which is set by the system owner. We demonstrate our approach by implementing a two-layered detection system, which uses supervised machine learning to identify phishing attacks. We evaluate our system with a dataset consisting of active phishing attacks and find that its performance is comparable to the state of the art.

Item Type:Article
ISSN:1424-8220
Uncontrolled Keywords:multi-layer ; phishing ; supervised machine learning
Group:Faculty of Science & Technology
ID Code:34517
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:08 Sep 2020 08:33
Last Modified:08 Sep 2020 08:33

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