Improved Random Forest Algorithm using Chicken Swarm Optimization for Phishing Website Classification Model
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 4 |
| Year of Publication : 2023 |
| Authors : C. Rajeswary, M. Thirumaran |
10.14445/23488379/IJEEE-V10I4P114
How to Cite?
C. Rajeswary, M. Thirumaran, "Improved Random Forest Algorithm using Chicken Swarm Optimization for Phishing Website Classification Model," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 4, pp. 141-151, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I4P114
Abstract:
Phishing is a type of online fraud which enables attackers to trick individuals into giving away confidential data like login credentials or financial data. A phishing website utilizes a URL that is comparable to a reasonable website to trick users into thinking it is legitimate, or it may comprise suspicious links or forms which has been developed for collecting sensitive data from users. Machine learning (ML) can be utilized to categorise websites as phishing or legitimate to protect users from falling victim to these attacks. There are several approaches to using machine learning for phishing website classification. This article focuses on the design of Chicken Swarm Optimization with Improved Random Forest for Phishing Website Classification (CSOIRF-PWC) technique. The CSOIRF-PWC technique aims to discriminate the legitimate and phishing websites accurately. To execute this, the presented CSOIRF-PWC approach initially performs the data normalization process. Next, the classification of phishing websites takes place using the IRF classifier. For improving the classification performance of the RF classifier, the parameter tuning process is performed through the CSO algorithm, which supports attaining improved classification performance. The simulation values of the CSOIRF-PWC methodology are investigated on two datasets, and the outputs are investigated under diverse measures. The comprehensive comparative outcomes emphasized the enhanced performance of the CSOIRF-PWC system over other methodologies in terms of several measures.
Keywords:
Phishing websites, Classification models, Random forest, Chicken swarm optimization, Machine learning, Security.
References:
[1] Ammar Almomani, “Phishing Website Detection with Semantic Features Based on Machine Learning Classifiers: A Comparative Study,” International Journal on Semantic Web and Information Systems (IJSWIS), vol. 18, no. 1, pp. 1-24.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Lizhen Tang, and Qusay H. Mahmoud, “A Survey of Machine Learning-Based Solutions for Phishing Website Detection,” Machine Learning and Knowledge Extraction, vol. 3, no. 3, pp. 672-694, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Zeyad Ghaleb Al-Mekhlafi et al., “Phishing Websites Detection by Using Optimized Stacking Ensemble Model,” Computer Systems Science and Engineering, vol. 41, no. 1, pp. 109-125, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Aditya Kharat et al., "Implementation of Defence Schemes for Phishing Attacks on Mobile Devices," SSRG International Journal of Computer Science and Engineering, vol. 6, no. 6, pp. 28-34, 2019.
[CrossRef] [Publisher Link]
[5] Ammar Odeh, Ismail Keshta, and Eman Abdelfattah, “Machine Learningtechniquesfor Detection of Website Phishing: A Review for Promises and Challenges,” In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0813- 0818, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Yazan A. Alsariera, “Intelligent Tree-Based Ensemble Approaches for Phishing Website Detection,” Journal of Engineering Science and Technology, vol. 17, no. 1, pp. 563-582, 2022.
[Google Scholar] [Publisher Link]
[7] Charu Singh, and Meenu, “Phishing Website Detection Based on Machine Learning: A Survey,” In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 398-404, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Arriane Livara, and Rowell Hernande, “An Empirical Analysis of Machine Learning Techniques In Phishing E-Mail Detection,” In 2022 International Conference for Advancement In Technology (ICONAT), pp. 1-6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] R. Selvaraj et al., "Optimized Machine Learning for CHD Detection Using 3D CNN-Based Segmentation, Transfer Learning and Adagrad Optimization," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 3, pp. 20-34, 2023.
[CrossRef] [Publisher Link]
[10] Rajesh Singh et al., "Internet of Wild Things with the Integration of Vision Technology and Lora Network," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 3, pp. 1-7, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] A N Shilpa, and C S Veena, "A Hybrid Compressive Sensing Network for ROI-Based Medical Image Recovery," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 3, pp. 8-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Mohammad A. Alsharaiah et al., “A New Phishing-Website Detection Framework Using Ensemble Classification and Clustering,” International Journal of Data and Network Science, vol. 7, no. 2, pp. 857-864, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Shatha Ghareeb et al., “Analysis of Feature Selection and Phishing Website Classification Using Machine Learning,” In 2023 15th International Conference on Developments in Esystems Engineering (DeSe), pp. 178-183, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Dr. Ananthi Sheshasaayee, and V.Vidyapriya, "Reorganisation of Adaptive Websites Using Web Usage Mining Techniques,” International Journal of Computer & Organization Trends, vol. 4, no. 3, pp. 53-58, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Anitha, J., and Kalaiarasu, M., “A New Hybrid Deep Learning-Based Phishing Detection System Using MCS-DNN Classifier,” Neural Computing and Applications, vol. 34, no. 8, pp. 5867-5882.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Mohammed Al-Sarem et al., “An Optimized Stacking Ensemble Model for Phishing Websites Detection,” Electronics, vol. 10, no. 11, p. 1285.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Ali Aljofey et al., “An Effective Detection Approach for Phishing Websites Using URL and HTML Features,” Scientific Reports, vol. 12, no. 1, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Abdulhamit Subasi, Emir Kremic, “Comparison of Adaboost with Multiboosting for Phishing Website Detection,” Procedia Computer Science, vol. 168, pp. 272-278, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Peravali Kavya, "An Efficient Machine Learning Based Algorithm for Preventing Phishing Websites," SSRG International Journal of Computer Science and Engineering, vol. 5, no. 12, pp. 10-13, 2018.
[CrossRef] [Publisher Link]
[20] Altyeb Taha, “Intelligent Ensemble Learning Approach for Phishing Website Detection Based on Weighted Soft Voting,” Mathematics, vol. 9, no. 21, p. 2799, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Jordan Stobbs, Biju Issac, and Seibu Mary Jacob, “Phishing Web Page Detection Using Optimised Machine Learning,” In 2020 IEEE 19th International Conference on Trust, Security and Privacy In Computing and Communications (TrustCom) , pp. 483-490, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Jebakumar Immanuel et al., "The Effectiveness of Security Images In Internet Banking,” International Journal of P2P Network Trends and Technology, vol. 7, no. 3, pp. 6-9, 2017.
[Google Scholar] [Publisher Link]
[23] Xuqiao Yu et al., “Phishing Websites Detection Based on Hybrid Model of Deep Belief Network and Support Vector Machine,” In IOP Conference Series: Earth and Environmental Science , vol. 602, no. 1, p. 012001, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Kailong Liu, “Feature Analyses and Modeling of Lithium-Ion Battery Manufacturing Based on Random Forest Classification,” IEEE/ASME Transactions on Mechatronics, vol. 26, no. 6, pp.2944-2955.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Zhi-Feng Liu et al., “Prediction Short-Term Photovoltaic Power Using Improved Chicken Swarm Optimizer-Extreme Learning Machine Model,” Journal of Cleaner Production, vol. 248, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Ameerabanu .B, and Niaz Ahamed .V .M, "Authenticated Framework for Security Based on Imperceptible Captcha," SSRG International Journal of Computer Science and Engineering, vol. 3, no. 5, pp. 38-42, 2016.
[CrossRef] [Publisher Link]
[27] Rundong Yang et al., “Phishing Website Detection Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning,” Sensors, vol. 21, no. 24, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Ali Aljofey et al., “An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL,” Electronics, vol. 9, no. 9, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Muna Elsadig et al., “Intelligent Deep Machine Learning Cyber Phishing URL Detection Based on BERT Features Extraction,” Electronics, vol. 11, p. 3647, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Mohammad Almseidin et al., “Cyber-Phishing Website Detection Using Fuzzy Rule Interpolation,” Cryptography, vol. 6, no. 2, p. 24, 2022.[CrossRef] [Google Scholar] [Publisher Link]