Open Access

ARTICLE

Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

by Hadil Shaiba¹, Jaber S. Alzahrani², Majdy M. Eltahir³, Radwa Marzouk⁴, Heba Mohsen⁵, Manar Ahmed Hamza^6,*

1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
3 Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
4 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
5 Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2022, 73(3), 6425-6441. https://doi.org/10.32604/cmc.2022.031625

Received 22 April 2022; Accepted 07 June 2022; Issue published 28 July 2022

Abstract

Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords, account identifiers, bank details, etc. In general, these kinds of cyberattacks are made at users through phone calls, emails, or instant messages. The anti-phishing techniques, currently under use, are mainly based on source code features that need to scrape the webpage content. In third party services, these techniques check the classification procedure of phishing Uniform Resource Locators (URLs). Even though Machine Learning (ML) techniques have been lately utilized in the identification of phishing, they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing offenses. The tremendous growth and evolution of Deep Learning (DL) techniques paved the way for increasing the accuracy of classification process. In this background, the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification (HSOHDL-PDC) model. The presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website URLs. In addition, SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no importance. Moreover, a hybrid Convolutional Neural Network-Long Short Term Memory (HCNN-LSTM) technique is also applied for identification and classification of phishing. The hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved outcomes. The performance of the proposed HSOHDL-PDC model was validated using different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.

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