IntAnti-Phish: An Intelligent Anti-Phishing Framework Using Backpropagation Neural Network

Sheikh Shah Mohammad Motiur Rahman, Lakshman Gope, Takia Islam, Mamoun Alazab

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Among the cybercriminals, the popularity of phishing has been rapidly growing day by day. Therefore, phishing has become an alarming issue to solve in the field of cybersecurity. Many researchers have already proposed several anti-phishing approaches to detect phishing in terms of email, webpages, images, or links. This study also aimed to propose and implement an intelligent framework to detect phishing URLs (Uniform Resource Locator). It has been observed in this study that Backpropagation Neural Network-based systems need to tune various hyperparameters to obtain the optimized output. With a maximum of two hidden layers along with 400 epochs can reach maximum accuracy of 0.93, the minimum mean squared error of 0.27, and also a minimum error rate of 0.07 which measurements lead this study to generate an optimized model for phishing detection. The detailed process of feature extraction and optimized model generation along with the detection of unknown URLs are considered and proposed during the development of IntAnti-Phish (An Intelligent Anti-Phishing Framework).

Original languageEnglish
Title of host publicationMachine Intelligence and Big Data Analytics for Cybersecurity Applications
EditorsYassine Maleh, Mohammad Shojafar, Mamoun Alazab, Youssef Baddi
Place of PublicationCham, Switzerland
PublisherSpringer Nature Switzerland AG
Number of pages14
ISBN (Electronic)978-3-030-57024-8
ISBN (Print)978-3-030-57023-1
Publication statusPublished - 2021

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503


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