Optimized Deep Learning Model Using Binary Particle Swarm Optimization for Phishing Attack Detection: A Comparative Study
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Phishing attacks manipulate users to disclose critical information, resulting in cybersecurity risks. Traditional phishing detection algorithms usually have large false positive rates and poor feature selection, degrading performance. This paper presents an optimized phishing detection framework that integrates binary particle swarm optimization (BPSO)-based feature selection (FS) with deep learning models. Six deep learning architectures were evaluated on the selected feature subset to identify the most effective model for accurate phishing classification. BPSO was used to select suitable attributes on a public Kaggle dataset with 10,000 samples, comprising phishing and legitimate website data with 48 attributes. NumDots, UrlLength, IpAddress, and NoHttps were selected among the 25 features chosen. BPSO was chosen because it effectively reduces feature dimensionality while preserving crucial attributes that enhance classification accuracy. The BPSO optimally selects relevant phishing-related attributes, improving model efficiency and reducing computational complexity. The BPSO technique optimally selects the most relevant features, reducing dataset dimensionality by 48% while maintaining high classification performance. We used six DL models—MLP, 1D-CNN, RNN, LSTM, GRU, and DNN—to test the specified characteristics. The experimental results demonstrate that the DNN model outperforms the other methods through 99.63% accuracy, 99.74% precision, 99.54% recall, and an AUC of 0.9999.
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[1] N. Sun, M. Ding, J. Jiang, W. Xu, X. Mo, et al., “Cyber Threat Intelligence Mining for Proactive Cybersecurity Defense: A Survey and New Perspectives,” IEEE Communications Surveys & Tutorials, vol.25, no.3, pp.1748-74, May 2023. https://doi.org/110.1109/COMST.2023.3273282.
[2] T. Nagunwa, “Comparative Analysis of Nature-Inspired Metaheuristic Techniques for Optimizing Phishing Website Detection,” Analytics, vol.3, no.3, pp.344-367, August 2024. https://doi.org/10.3390/analytics3030019
[3] P. Pathak and A. K. Shrivas, Development of Proposed Model Using Random Forest with Optimization Technique for Classification of Phishing Website, vol.5, no.1059, November 2024. https://doi.org/10.1007/s42979-024-03388-x
[4] M. Shujairi, “Developing IoT Performance in Healthcare Through the Integration of Machine Learning and Software-Defined Networking (SDN),” Babylonian Journal of Internet of Things, vol.2025, pp.77-88, 2025. https://doi.org/10.58496/BJIoT/2025/003
[5] K. M. K. Raghunath, V. V. Kumar, M. Venkatesan, K. K. Singh, and A. Singh, “XGBoost Regression Classifier (XRC) Model for Cyber Attack Detection and Classification Using Inception V4,” Journal of Web Engineering, vol.21, no.4, pp.1295 - 1322, June 2022. https://doi.org/10.13052/jwe1540-9589.21413
[6] G. Ali, W. Robert, M. M. Mijwil, M. Sallam, J. Ayad, and I. Adamopoulos, “Securing the Internet of Wetland Things (IoWT) Using Machine and Deep Learning Methods: A Survey,” Mesopotamian Journal of Computer Science, vol. 2025, pp.17–63, 2025. https://doi.org/10.58496/MJCSC/2025/002
[7] S. D. Guptta, K. T. Shahriar, H. Alqahtani, D. Alsalman, and I. H. Sarker, “Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques,” Annals of Data Science, vol.11, no.1, pp.217-42, March 2024. https://doi.org/10.1007/s40745-022-00379-8
[8] S. Alnemari and M. Alshammari, “Detecting Phishing Domains Using Machine Learning,” Applied Sciences, vol.13, no.8, pp. 4649, April 2024. https://doi.org/10.3390/app13084649
[9] E. Kocyigit, M. Korkmaz, O. Koray Sahingoz, and B. Diri, “Enhanced Feature Selection Using Genetic Algorithm for Machine-Learning-Based Phishing URL Detection,” Applied Sciences, vol.14, no.14, pp.6081, July 2024. https://doi.org/10.3390/app14146081
[10] U. A. Butt, R. Amin, H. Aldabbas, S. Mohan, B. Alouffi, and A. Ahmadian, “Cloud-based email phishing attack using machine and deep learning algorithm,” Complex & Intelligent Systems, vol.9, pp.3043–3070, June 2022. https://doi.org/10.1007/s40747-022-00760-3
[11] A. Maci, A. Santorsola, A. Coscia, and A. Iannacone, “Unbalanced Web Phishing Classification through Deep Reinforcement Learning,” Computers, vol.12, no.6, pp.118, June 2023. https://doi.org/10.3390/computers12060118
[12] S. Atawneh and H. Aljehani, “Phishing Email Detection Model Using Deep Learning,” Electronics, vol.12, no.20, pp.4261, October 2023. https://doi.org/10.3390/electronics12204261
[13] Z. Alshingiti, R. Alaqel, J. Al-Muhtadi, Q. E. U. Haq, K. Saleem, and M. H. Faheem, “A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN,” Electronics, vol.12, no.1, pp.232, January 2023. https://doi.org/10.3390/electronics12010232
[14] U. Zara, K. Ayyub, H. U. Khan, A. Daud, and T. Alsahfi, “Phishing Website Detection Using Deep Learning Models,” IEEE Access, vol.12, pp.167072 - 167087, October 2024. https://doi.org/10.1109/access.2024.3486462
[15] S. R. A. Samad, S. Balasubaramanian, A. S. Al-Kaabi, B. Sharma, S. Chowdhury, A. Mehbodniya, et al., “Analysis of the Performance Impact of Fine-Tuned Machine Learning Model for Phishing URL Detection,” Electronics, vol.12, no.7, pp.1642, March 2023. https://doi.org/10.3390/electronics12071642
[16] D. R. I. M. Setiadi, S. Widiono, A. N. Safriandono, and S. Budi, “Phishing Website Detection Using Bidirectional Gated Recurrent Unit Model and Feature Selection,” Journal of Future Artificial Intelligence and Technologies, vol.1, no.2, pp.75-83, July 2024. https://doi.org/10.62411/faith.2024-15
[17] S. Jamal, H. Wimmer, and I. H. Sarker, “An improved transformer-based model for detecting phishing, spam and ham emails: A large language model approach,” Security and Privacy, vol.7, no.5, pp.e402, April 2024. https://doi.org/10.1002/spy2.402
[18] A. Aljofey, Q. Jiang, Q. Qu, M. Huang, and J-P. Niyigena, “An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL,” Electronics, vol.9, no.9, pp.1514, September 2020. https://doi.org/10.3390/electronics9091514
[19] E. M. Elkenawy, A. A. Alhussan, D. S. Khafaga, Z. Tarek, and A. M. Elshewey, “Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification,” Scientific Reports, vol.14, no.23784, pp.1-23, October 2024. https://doi.org/10.1038/s41598-024-72013-x
[20] A. Mughaid, S. AlZu’bi, A. Hnaif, S. Taamneh, A. Alnajjar, and E. A. Elsoud, “An intelligent cyber security phishing detection system using deep learning techniques,” Cluster Computing, vol.25, pp.3819–3828, May 2022. https://doi.org/10.1007/s10586-022-03604-4
[21] M. Almousa, T. Zhang, A. Sarrafzadeh, and M. Anwar, “Phishing website detection: How effective are deep learning-based models and hyperparameter optimization?,” Security and Privacy, vol.5, no.6, pp.e256, August 2022. https://doi.org/10.1002/spy2.256
[22] L. Lakshmi, M. P. Reddy, C. Santhaiah, and U. J. Reddy, “Smart Phishing Detection in Web Pages using Supervised Deep Learning Classification and Optimization Technique ADAM,” Wireless Personal Communications, vol.118, pp.3549–3564, March 2021. https://doi.org/10.1007/s11277-021-08196-7
[23] F. Aljuaydi, B. K. Behera, A. M. Elshewey, and Z. Tarek, “A Deep Learning Prediction Model to Predict Sustainable Development in Saudi Arabia,” Applied Mathematics & Information Sciences, vol.18, no.6, pp.1345-1366, 2024. http://dx.doi.org/10.18576/amis/180615
[24] D. Han, H. Li, and X. Fu, “Reflective Distributed Denial of Service Detection: A Novel Model Utilizing Binary Particle Swarm Optimization—Simulated Annealing for Feature Selection and Gray Wolf Optimization-Optimized LightGBM Algorithm,” Sensors, vol.24, no.19, pp.6179, September 2024. https://doi.org/10.3390/s24196179
[25] A. J. S. Albahadili, A. Akbas, and J. Rahebi, “Detection of phishing URLs with deep learning based on GAN-CNN-LSTM network and swarm intelligence algorithms,” Signal, Image and Video Processing, vol.18, pp.4979-95, June 2024. https://doi.org/10.1007/s11760-024-03204-2
[26] A. Denis, A. Thomas, W. Robert, A. Samuel, S. Peter Kabiito, Z. Morish, M. Sallam, G. Ali, and M. M. Mijwil, “A Survey on Artificial Intelligence and Blockchain Applications in Cybersecurity for Smart Cities,” SHIFRA, vol.2025, pp.1-45, Januanry 2025. https://doi.org/10.70470/SHIFRA/2025/001
[27] T. Karthikeyan, M. Govindarajan, and V. Vijayakumar, “An effective fraud detection using competitive swarm optimization based deep neural network,” Measurement: Sensors, vol.27, pp.100793, June 2023. https://doi.org/10.1016/j.measen.2023.100793
[28] P. H. Kyaw, J. Gutierrez, and A. Ghobakhlou, “A Systematic Review of Deep Learning Techniques for Phishing Email Detection,” Electronics, vol.13, no.19, pp.3823, September 2024. https://doi.org/10.3390/electronics13193823
[29] J. Aljabri, N. Alzaben, N. NEMRI, S. Alahmari, S. D. Alotaibi, S. Alazwari, et al., “Hybrid stacked autoencoder with dwarf mongoose optimization for Phishing attack detection in internet of things environment,” Alexandria Engineering Journal, vol.106, pp.164-171, November 2024. https://doi.org/10.1016/j.aej.2024.06.070
[30] S. Minocha and S. Birmohan, “A novel phishing detection system using binary modified equilibrium optimizer for feature selection,” Computers & Electrical Engineering, vol. 98, pp.107689., 2022. https://doi.org/10.1016/j.compeleceng.2022.107689.
[31] G. Mohamed, J. Visumathi, M. Mahdal, J. Anand, and M. Elangovan, “An Effective and Secure Mechanism for Phishing Attacks Using a Machine Learning Approach,” Processes, vol.10, no.7, pp.1356, July 2022. https://doi.org/10.3390/pr10071356
[32] F. Feng, Q. Zhou, Z. Shen, X. Yang, L. Han, and J.Q. Wang, “The application of a novel neural network in the detection of phishing websites,” Journal of Ambient Intelligence and Humanized Computing, vol.15, pp. 1865–1879, April 2018. https://doi.org/10.1007/s12652-018-0786-3
[33] K. Bitirgen and Ü. B. Filik “A hybrid deep learning model for discrimination of physical disturbance and cyber-attack detection in smart grid,” International Journal of Critical Infrastructure Protection, vol.40, pp.100582, March 2023. https://doi.org/10.1016/j.ijcip.2022.100582
[34] N. Kunhare, R. Tiwari, and J. Dhar, “Particle swarm optimization and feature selection for intrusion detection system,” Sādhanā, vol.45, pp.109, May 2020. https://doi.org/10.1007/s12046-020-1308-5
[35] S. Jagan, A. Ashish, M. Mahdal, K. R. Isabels, et al., “A Meta-Classification Model for Optimized ZBot Malware Prediction Using Learning Algorithms,” Mathematics, vol.11, no.13, pp.2840, June 2023. https://doi.org/10.3390/math11132840
[36] A. K. Balyan, S. Ahuja, U. K. Lilhore, S. K. Sharma, P. Manoharan, et al., “A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method,” Sensors, vol.22, no.16, pp.5986, August 2022. https://doi.org/10.3390/s22165986
[37] T. R. Alsenani, S. I. Ayon, S. M. Yousuf, F. B. K. Anik, and M. E. S. Chowdhury, “Intelligent feature selection model based on particle swarm optimization to detect phishing websites,” Multimedia Tools and Applications, vol.82, pp.44943–44975, April 2023. https://doi.org/10.1007/s11042-023-15399-6
[38] C. Yang, W. Guan, and Z. Fang, “IoT Botnet Attack Detection Model Based on DBO-Catboost,” Applied Sciences, vol.13, no.12, pp.7169, June 2023. https://doi.org/10.3390/app13127169
[39] S. K. Towfek, “CNN-Based Multiclass Classification for COVID-19 in Chest X-ray Images,” Journal of Artificial Intelligence and Metaheuristics, vol. 6, no. 1, pp. 48-55, 2023. https://doi.org/10.54216/jaim.060105
[40] M. Abotaleb, W. H. Lim, P. Mishra, A. T. Qenawy, and E. M. ALmetwally, “Enhancing Stock Price Prediction Accuracy Using ARIMA and Advanced Greylag Goose Optimizer Algorithm,” Journal of Artificial Intelligence in Engineering Practice, vol.1, no.1, pp. 49-65, April 2024. https://doi.org/10.21608/jaiep.2024.355004
[41] N. Innab, A. A. F. Osman, M. A. M. Ataelfadiel, M. A-Zanona, B. M. Elzaghmouri, et al., “Phishing Attacks Detection Using Ensemble Machine Learning Algorithms,” Computers, Materials & Continua, vol.80, no.1, pp.1325-1245, 2024. https://doi.org/10.32604/cmc.2024.051778
[42] S. A. Elsaid, E. Shehab, A. M. Mattar, A. T. Azar, and I. A. Hameed, “Hybrid intrusion detection models based on GWO optimized deep learning,” Discover Applied Sciences, vol.6, no.531, pp.1-34, October 2024. https://doi.org/10.1007/s42452-024-06209-1
[43] M. F. Alghenaim, N. A. A. Bakar, and F. A. Rahim, “Anti-Phishing Tools: State of the Art and Detection Efficiencies,” Applied Mathematics & Information Sciences, vol.16, no.6, pp. 929-34, 2022. https://doi.org/10.18576/amis/160609
[44] M. A. Elberri, Ü. Tokeşer, J. Rahebi and J. M. Lopez-Guede, “A cyber defense system against phishing attacks with deep learning game theory and LSTM-CNN with African vulture optimization algorithm (AVOA),” International Journal of Information Security, vol.23, no.4, pp. 2583-606, May 2024. https://doi.org/10.1007/s10207-024-00851-x
[45] A. B. Majgave, and N. L. Gavankar, “Automatic Phishing Website Detection and Prevention Model Using Transformer Deep Belief Network,” Computers & Security, vol. 147, pp.104071, 2024. https://doi.org/10.1016/j.cose.2024.104071
[46] L. Das, L. Ahuja, and A. Pandey, “A novel deep learning model-based optimization algorithm for text message spam detection,” The Journal of Supercomputing, vol.80, pp.17823–17848, May 2024. https://doi.org/10.1007/s11227-024-06148-z
[47] S. Anupam and A. K. Kar, “Phishing website detection using support vector machines and nature-inspired optimization algorithms,” Telecommunication Systems, vol.76, pp.17-32, November 2020. https://doi.org/10.1007/s11235-020-00739-w
[48] Y. Fouad, N. E. Abdelaziz, and A. M. Elshewey, “IoT Traffic Parameter Classification based on Optimized BPSO for Enabling Green Wireless Networks,” Engineering Technology & Applied Science Research, vol. 14, no. 6, pp. 18929-34, 2024. https://doi.org/10.48084/etasr.9230
[49] A. M. Salman, H. I. Wahhab, A. B. Alnajjar, B. Al-Attar, R. Sekhar, and N. Itankar, “Revolutionizing Wireless Sensor Networks through an Effective Approach for Quality of Service Enhancement,” Applied Data Science and Analysis, vol.2025, pp.144-154, April 2025. https://doi.org/10.58496/ADSA/2025/012
[50] A, Desai and M. Desai, “A Review of the State of Cybersecurity in the Healthcare Industry and Propose Security Controls,” Mesopotamian Journal of Artificial Intelligence in Healthcare, vol.2023, pp.82-84, 2023. https://doi.org/10.58496/MJAIH/2023/016
[51] N. Kamble and N. Mishra, “Hybrid Optimization Enabled Squeeze Net for Phishing Attack Detection,” Computers & Security, vol. 144, pp.103901, May 2024. https://doi.org/10.1016/j.cose.2024.103901.
[52] M. Nanda and S. Goel, “URL based phishing attack detection using BiLSTM-gated highway attention block convolutional neural network,” Multimedia Tools and Applications, vol.83, pp.69345–69375, January 2024. https://doi.org/10.1007/s11042-023-17993-0
[53] S. Jafari and N. Aghaee‐Maybodi. “Detection of Phishing Addresses and Pages With a Data Set Balancing Approach by Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) Optimized With Swarm Intelligence,” Concurrency and Computation Practice and Experience, vol. 36, no. 11, Jan. 2024, https://doi.org/10.1002/cpe.8033