@inbook{dc7e1c3c4ed94e409177d2c78fc830e3,
title = "IntAnti-Phish: An Intelligent Anti-Phishing Framework Using Backpropagation Neural Network",
abstract = "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).",
keywords = "Anti-phishing framework, Backpropagation, Intelligent phishing detection, Malicious URLs detection, Neural network",
author = "Rahman, {Sheikh Shah Mohammad Motiur} and Lakshman Gope and Takia Islam and Mamoun Alazab",
year = "2021",
doi = "10.1007/978-3-030-57024-8_9",
language = "English",
isbn = "978-3-030-57023-1",
series = "Studies in Computational Intelligence",
publisher = "Springer Nature Switzerland AG",
pages = "217--230",
editor = "Yassine Maleh and Mohammad Shojafar and Mamoun Alazab and Youssef Baddi",
booktitle = "Machine Intelligence and Big Data Analytics for Cybersecurity Applications",
address = "Switzerland",
edition = "1",
}