Phishing Detection with Browser Extension Based on Machine Learning

Che Yu Wu, Cheng Chung Kuo, Chu Sing Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social engineering has become an emergent field with the growth of network attacks. Phishing, is a popular social engineering attack types, has also become an important research topic in the network security field. In this study, we implemented a browser extension for detecting phishing websites based on a machine learning mechanism. The proposed front-end browser extension can detect phishing websites in real time and hence, prevent users from inputting their personal information to these sites. The experimental results show that the proposed detection mechanism can reach a high accuracy up to 97.5% within 4.5 s with 150 users on average.

Original languageEnglish
Title of host publicationProceedings - 2023 18th Asia Joint Conference on Information Security, AsiaJCIS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages81-87
Number of pages7
ISBN (Electronic)9798350341638
DOIs
Publication statusPublished - 2023
Event18th Asia Joint Conference on Information Security, AsiaJCIS 2023 - Hybrid, Tokyo, Japan
Duration: 2023 Aug 152023 Aug 16

Publication series

NameProceedings - 2023 18th Asia Joint Conference on Information Security, AsiaJCIS 2023

Conference

Conference18th Asia Joint Conference on Information Security, AsiaJCIS 2023
Country/TerritoryJapan
CityHybrid, Tokyo
Period23-08-1523-08-16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Safety, Risk, Reliability and Quality

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