Title: Machine learning approach for classification of phishing attacks with particle swarm optimisation technique

Authors: Prakash Pathak; Akhilesh Kumar Shrivas

Addresses: Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh, 495009, India ' Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh, 495009, India

Abstract: Phishing is an online scam where an attacker creates fake websites or emails to collect secret information from the internet or email users. The main contribution of research work is to develop a robust and computationally efficient hybrid model using machine learning based classification techniques with particle swarm optimisation (PSO) to facilitate the classification of phishing attacks. The study constructs a machine learning-based ensemble model empowered by particle swarm optimisation for effective phishing attack classification. A novel ensemble model is developed, amalgamating support vector machine (SVM), logistic regression (LR), and decision trees (DT) through a voting scheme ensemble technique. Additionally, PSO feature optimisation technique are applied to phishing dataset to streamline feature set. Comparative analysis with existing classifiers and ensemble models, employing reduced feature set, demonstrates that our proposed model achieves a remarkable 99.08% accuracy with 27 features. Consequently, our recommended model offers expedited computational time for phishing attack classification.

Keywords: phishing attacks; machine learning; classification; proposed model; PSO; particle swarm optimisation; 10-fold cross-validation.

DOI: 10.1504/IJCSM.2025.149610

International Journal of Computing Science and Mathematics, 2025 Vol.22 No.1, pp.50 - 75

Received: 08 Jun 2024
Accepted: 15 Jun 2025
Published online: 07 Nov 2025 *

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