Abstract
Phishing attacks usually copy reliable websites, such as banks and financial institutions, in an attempt to obtain personal information, such as passwords and credit card details. This study suggests a hybrid phishing detection system that combines the Back Propagation Neural Network (BPNN) for classification with XGBoost for feature selection. For training (80%) and testing (20%), a dataset of 11,000 URLs was employed, including both phishing and authentic samples. Important URL-based characteristics were extracted, including URL length, discrepancy character, HTTPS appearance, and domain age. High identification accuracy (97.5%), precision (96.8%), recall (98.2%), and F1-score (97.5%) were obtained by all systems. When compared to traditional classifiers (SVM, Random Forest), the proposed model shows better performance in identifying zero-day phishing efforts, Explanation measures were used to assess the model, and Scikit-LARN was used to simulate it in python. The results confirm that the algorithm can successfully detect and stop phishing efforts in real time.
References
- Singhal, Siddharth, Utkarsh Chawla, and Rajeev Shorey. "Machine learning & concept drift based approach for malicious website detection." In 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), IEEE, 2020, 582-585.
- Sanaa Kaddoura, Classification of malicious and benign websites by network features using supervised machine learning algorithms, Cyber Security in Networking Conference, 2021.
- Kaddoura, Sanaa. "Classification of malicious and benign websites by network features using supervised machine learning algorithms." In 2021 5th Cyber Security in Networking Conference (CSNet), IEEE, 2021, 36-40.
- Sushma, K. S. N., M. Jayalakshmi, and Tapas Guha. "Deep learning for phishing website detection." In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), IEEE, 2022, 1-6.
- Chen, Shaoming, Han Wu, Xingkai Cheng, and Liyan Mao. "Detecting Phishing Websites by Using a Hybrid Method of Page Content and Formal Concept Analysis." In 2023 7th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE), IEEE, 2023, 778-781.
- Deepak Rao Khadatkar; Yugmani Sahu; Nivesh Pole; Vinay Nayak, Detection of Healthcare Phishing Websites Using Machine Learning, International Conference on Artificial Intelligence for Innovations in Healthcare Industries, 2023.
- Hu, Qiang, Hangxia Zhou, and Qian Liu. "Phishing website detection based on multi-feature stacking." In 2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE), IEEE, 2021, 716-720.
- Bikku, Thulasi, Mude Nikitha, Anjali Vajja, Kanneganti Harshitha, and Jhansi Rani. "Optimized machine learning algorithm to classify phishing websites." In 2022 International Conference on Electronics and Renewable Systems (ICEARS), IEEE, 2022, 1148-1152.
- Almousa, May, Ruben Furst, and Mohd Anwar. "Characterizing coding style of phishing websites using machine learning techniques." In 2022 Fourth International Conference on Transdisciplinary AI (TransAI), IEEE, 2022, 101-105.
- Noh, Norzaidah Binti Md, and M. Nazmi Bin M. Basri. "Phishing website detection using random forest and support vector machine: a comparison." In 2021 2nd International Conference on Artificial Intelligence and Data Sciences (AIDAS), IEEE, 2021, 1-5.
- Sharma, Khushal, Pratik Rai, and Jyoti Chandel. "Review Paper Real-Time Phishing Website With Machine Learning." In 2023 11th International Conference on Intelligent Systems and Embedded Design (ISED), IEEE, 2023, 1-5.
- Akhas Rahmadeyan; Mustakim; Imam Ahmad; Allan Desi Alexander; Alkautsar Rahman, Phishing Website Detection with Ensemble Learning Approach Using Artificial Neural Network and AdaBoost, International Conference on Information Technology Research and Innovation (ICITRI), 2023.
- Jaswal, Prajwal, Shweta Sharma, Naveen Bindra, and C. Rama Krishna. "Detection and Prevention of Phishing Attacks on Banking Website." In 2022 International Conference on Futuristic Technologies (INCOFT), IEEE, 2022, 1-8.
- Dharmaraju, Gangu, Tatapudi Nirosh Kumar, P. PattabhiRama Mohan, Raja Rao Pbv, and A. Lakshmanarao. "Phishing website detection through ensemble machine learning techniques." In 2024 2nd International Conference on Computer, Communication and Control (IC4), IEEE, 2024, 1-5.
- Gu, Chenyu. "A lightweight phishing website detection algorithm by machine learning." In 2021 International Conference on Signal Processing and Machine Learning (CONF-SPML), IEEE, 2021, 245-249.
- Jie, Xu, Lan Haoliang, and Ju Ao. "A new model for simultaneous detection of phishing and darknet websites." In 2021 7th International Conference on Computer and Communications (ICCC), IEEE, 2021, 2002-2006.
- MohammedM Elsheh; Khadija Swayeb, Phishing Website Detection Using a Hybrid Approach Based on Support Vector Machine and Ant Colony Optimization, IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), 2023.
- Jain, Saumya, and Chetan Gupta. "A support vector machine learning technique for detection of phishing websites." In 2023 6th International Conference on Information Systems and Computer Networks (ISCON), IEEE, 2023, 1-6.
- Zin, Nurul Amira Binti Mohd, Mohd Faizal Ab Razak, Ahmad Firdaus, Ferda Ernawan, and Nor Saradatul Akmar Zulkifli. "Machine learning technique for phishing website detection." In 2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS), IEEE, 2023, 235-239.
- Ghareeb, Shatha, Mohamed Mahyoub, and Jamila Mustafina. "Analysis of feature selection and phishing website classification using machine learning." In 2023 15th International Conference on Developments in eSystems Engineering (DeSE), IEEE, 2023, 178-183.