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      Towards a Multi-Layered Phishing Detection

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          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.

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          An intrusion detection system for connected vehicles in smart cities

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            Machine Learning Based Phishing Detection from URLs

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              Phishing detection based Associative Classification data mining

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                13 August 2020
                August 2020
                : 20
                : 16
                : 4540
                Affiliations
                [1 ]Department of Computing and Informatics, Bournemouth University, Bournemouth BH12 5BB, UK; s4908752@ 123456bournemouth.ac.uk
                [2 ]Department of Computing and Mathematical Sciences, University of Greenwich, London SE10 9BD, UK; a.nisioti@ 123456greenwich.ac.uk
                Author notes
                Article
                sensors-20-04540
                10.3390/s20164540
                7472607
                32823675
                8f7ef946-c649-4b57-97cf-7b73017caf4f
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 15 July 2020
                : 11 August 2020
                Categories
                Article

                Biomedical engineering
                supervised machine learning,phishing,multi-layer
                Biomedical engineering
                supervised machine learning, phishing, multi-layer

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