Phishing-Deception Data Model for Online Detection and Human Protection

Phoebe Barraclough, Graham Sexton

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The construction and interaction procedure of phishing and user in the deception mode is presented. We analyses phishing behavior when tempting human in order to construct a phishing-deception human-based data model (PDHDM) based on frequent associated events. The proposed phishing-deception human-based data model is utilized to generate association rules and to accurately classify between phishing and legitimate websites. This approach can reduce false positive rates in phishing detection systems, including a lack of effective dataset. Classification algorithms is employed for training and validation of the model. The proposed approach performance and the existing work is compared. Our proposed method yielded a remarkable result. The finding demonstrates that phishing-deception human-based data model is a promising scheme to develop effective phishing detection systems.
Original languageEnglish
Title of host publicationGlobal Security, Safety and Sustainability - The Security Challenges of the Connected World
PublisherSpringer
Pages144-154
Volume630
ISBN (Print)978-3-319-51063-7
DOIs
Publication statusPublished - 4 Jan 2017

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