Gargi Deshpande,
Shreyas Katkar,
Tanvi Kangane,
Ayush Kumar Giri,
Diksha Kale,
- Student, Department of Computer Engineering, Pillai College of Engineering, Dr. K. M. Vasudevan Pillai Campus, Plot No. 10, Sector 16, New Panvel East, Panvel, Navi Mumbai, Maharashtra, India
- Student, Department of Computer Engineering, Pillai College of Engineering, Dr. K. M. Vasudevan Pillai Campus, Plot No. 10, Sector 16, New Panvel East, Panvel, Navi Mumbai, Maharashtra, India
- Student, Department of Computer Engineering, Pillai College of Engineering, Dr. K. M. Vasudevan Pillai Campus, Plot No. 10, Sector 16, New Panvel East, Panvel, Navi Mumbai, Maharashtra, India
- Student, Department of Computer Engineering, Pillai College of Engineering, Dr. K. M. Vasudevan Pillai Campus, Plot No. 10, Sector 16, New Panvel East, Panvel, Navi Mumbai, Maharashtra, India
- Assistant Professor, Department of Computer Engineering, Pillai College of Engineering, Dr. K. M. Vasudevan Pillai Campus, Plot No. 10, Sector 16, New Panvel East, Panvel, Navi Mumbai, Maharashtra, India
Abstract
‘]
In the digital era, personal information theft has become a widespread and increasingly severe crime. Cybercriminals, often known as hackers, use deceptive strategies, with phishing websites being a major method for stealing confidential data. These fake websites imitate legitimate ones, tricking users into revealing sensitive personal and financial information, which has led to a rise in fraud cases. To address this escalating threat, a comprehensive research paper is proposed. This approach involves preprocessing datasets and analyzing attributes such as IP addresses, URL length, and web traffic statistics to differentiate phishing websites from genuine ones. Feature extraction is performed using deep learning methods, such as Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). A hybrid model that integrates machine learning with Transformer and GRU components is used, demonstrating better performance than traditional methods like LSTM, Naïve-Bayes, and Support Vector Machines (SVM). The study aims to identify phishing URLs and determine the most effective machine learning method by comparing each algorithm’s accuracy, false positive rate, and false negative rate. The proposed method was evaluated using 156,422 malicious sites and 392,924 legitimate sites, and it was found to be more effective in detecting malicious URLs compared to more recent methods.
Keywords: Phishing, Legitimate, CNN-GRU, LightGBM, Length Normalization, Uniform Encoding, Embedding Layer, Softmax
[This article belongs to Journal of Computer Technology & Applications (jocta)]
Gargi Deshpande, Shreyas Katkar, Tanvi Kangane, Ayush Kumar Giri, Diksha Kale. Detecting Phishing Websites Using Hybrid Methodologies. Journal of Computer Technology & Applications. 2024; 15(03):-.
Gargi Deshpande, Shreyas Katkar, Tanvi Kangane, Ayush Kumar Giri, Diksha Kale. Detecting Phishing Websites Using Hybrid Methodologies. Journal of Computer Technology & Applications. 2024; 15(03):-. Available from: https://journals.stmjournals.com/jocta/article=2024/view=171873
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References ‘]
- Yang P, Zhao G, Zeng P. Phishing website detection based on multidimensional features driven by deep learning. IEEE Access. 2019 Jan 11;7:15196-209.
- Odeh A, Keshta I, Abdelfattah E. Machine learning techniques for detection of website phishing: A review of promises and challenges. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC); 2021 Jan 27; Las Vegas, NV. IEEE; 2021. p. 0813-0818.
- Wei Y, Sekiya Y. Sufficiency of ensemble machine learning methods for phishing websites detection. IEEE Access. 2022 Nov 24;10:124103-13.
- Bhagwat MD, Patil PH, Vishwanath TS. A methodical overview on detection, identification, and proactive prevention of phishing websites. In: 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV); 2021 Feb 4; Coimbatore, India. IEEE; 2021. p. 1505-1508.
- Zhou J, Cui H, Li X, Yang W, Wu X. A novel phishing website detection model based on LightGBM and domain name features. Symmetry. 2023 Jan 7;15(1):180.
- Alshingiti Z, Alaqel R, Al-Muhtadi J, Haq QE, Saleem K, Faheem MH. A deep learning-based phishing detection system using CNN, LSTM, and LSTM-CNN. Electronics. 2023 Jan 3;12(1):232.
- Alswailem A, Alabdullah B, Alrumayh N, Alsedrani A. Detecting phishing websites using machine learning. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS); 2019 May 1; pp. 1-6. IEEE.
- Xu Y, Chen G, Liu Q, Xu W, Zhang L, Wu J, Fan X. A phishing website detection and recognition method based on naive Bayes. In: 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC); 2022 Mar 4; Vol. 6: pp. 1557-1562. IEEE.
- Yuan H, Chen X, Li Y, Yang Z, Liu W. Detecting phishing websites and targets based on URLs and webpage links. In: 2018 24th International Conference on Pattern Recognition (ICPR); 2018 Aug 20; pp. 3669-3674. IEEE.
- Korkmaz M, Sahingoz OK, Diri B. Detection of phishing websites by using machine learning-based URL analysis. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT); 2020 Jul 1; pp. 1-7. IEEE.
Journal of Computer Technology & Applications
| Volume | 15 |
| Issue | 03 |
| Received | June 26, 2024 |
| Accepted | August 12, 2024 |
| Published | September 12, 2024 |