Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm  

作　　者：Brij Bhooshan Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui 

机构地区：[1]Department of Computer Science and Information Engineering,Asia University,Taichung,413,Taiwan [2]Symbiosis Centre for Information Technology(SCIT),Symbiosis International University,Pune,Maharashtra,411057,India [3]Center for Interdisciplinary Research,University of Petroleum and Energy Studies(UPES),Dehradun,248007,India [4]Ronin Institute,Montclair,NJ 07043,USA [5]Management Department,College of Business Administration,Princess Nourah Bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [6]Department of Business Administration,Asia University,Taichung,413,Taiwan [7]Department of Electrical and Computer Engineering,Lebanese American University,Beirut,1102,Lebanon [8]Department of Computer Sciences,Faculty of Computing and Information Technology,Northern Border University,Rafha,91911,Saudi Arabia [9]Department of Electronic Engineering and Computer Science,Hong Kong Metropolitan University(HKMU),Homantin,Hong Kong,China

出　　处：《Computers, Materials & Continua》2024年第9期4895-4916,共22页计算机、材料和连续体（英文）

基　　金：Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R 343);PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia;Deanship of Scientific Research at Northern Border University,Arar,Kingdom of Saudi Arabia,for funding this researchwork through the project number“NBU-FFR-2024-1092-02”.

摘　　要：Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 attributes.The WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation process.The results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

关 键 词：Phishing detection Recurrent Neural Network(RNN) Whale Optimization Algorithm(WOA) CYBERSECURITY machine learning optimization 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]