An optimized Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) together (GA-PSO) method for email spam classification is presented in this paper. To improve classification accuracy and computing efficiency, the model combines The collective intelligence found in Particle Swarm Optimization (PSO).with the evolutionary powers of Genetic Algorithms (GA). The proposed GA-PSO classifier was rigorously tested over 400 cycles using datasets from Enron and Spam Assassin. Superior performance measures were attained by the model, including a 50% improvement in fitness margin, a 3% decrease in fitness error margin, and a computational efficiency that was five times faster than traditional techniques. By developing a strong, scalable algorithm with enhanced decision-making accuracy, this research advances spam detection and makes a substantial advancement in tackling email spam issues.

Email Spam, Machine Learning, Genetic Algorithm, Particle Swarm Optimization.

[1] A. Bhowick and S. M. Hazarika, “Machine Learning for E-Mail Spam Filtering: Review, Techniques and Trends,” Springer Nature, Vol.443, No.7, pp.1-8, 2017.
[2] A. Attar, R. M. Reza and R. E. Atani, “A survey of Image spamming and filtering techniques,” Springer Science + Business Media, Vol.2, No.40, pp.71-105, 2011.
[3] H. Faris, A.-Z. M. Ala, A. A. Heidari, I. Aljarah, M. Majdi, H. A. Mohammad and H. Fujita, “An Intelligent System for Spam Detection and Identification of the most Relevant Features based on Evolutionary Random Weight Networks,” Information Fusion, August, Vol.48, No.3, pp.67-83, 2019.
[4] V. S. Wakade, “Classification of Image Spam.,” OhioLINK Electronic, Vol.1, No.1, pp.1-5, 2011.
[5] O. E. Taylor and S. P. Ezekiel, “A Model to Detect Spam Email Using Support Vector Classifier and Random Forest Classifier,” International Journal of Computer Science and Mathematical Theory, Vol.6, No.1, pp.1-11, 2020.
[6] D. Melvin, T. Celik and C. Van Der Walt, “Unsupervised feature learning for spam email filtering,” Computers & Electrical Engineering, March, Vol.74, No.3, pp.89-104, 2019.
[7] G. Sanghani and K. Kotecha, “Incremental personalized E-mail spam filter using novel TFDCR feature selection with dynamic feature update,” Expert Systems with Applications, January, Vol.115, No.9, pp.287-299, 2019.
[8] O. H. Odukoya, O. B. Adedoyin, B. I. Akhigbe, T. A. Aladesanmi and G. A. Aderounmu, “An architectural-based approach to detecting spam in electronic means of communication,” Nigerian Journal of Technology, Vol.37, No.3, pp.1-5, 2018.
[9] P. Pandey, C. Agrawal and T. N. Ansari, “A Survey: Enhance Email Spam Filtering,” International Journal of Advanced Technology & Engineering Research (IJATER), Vol.8, No.1, pp.1-7, 2018.
[10] R. D. Warkar and I. R. Shaikh, “Review On: Detection of Spam Comments Using NLP Algorithm,” International Journal of Engineering And Computer Science, January, Vol.7, No.1, pp.23386-23489, 2018.
[11] F. Wentao, S. Bourouis, N. Bouguila, F. Aldosari, H. Sallay and J. K. Khayyat, “EP-Based Infinite Inverted Dirichlet Mixture Learning: Application to Image Spam Detection,” Springer Science, Vol.1, No.11, pp.342-354, 2018.
[12] I. Androutsopoulos, G. Paliouras, V. Karkaletsis, G. Sakkis, C. D. Spyropoulos and P. Stamatopoulous, “Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach,” in the proceedings from the fourth European Conference on Principles and Practice of Knowledge Discovery in Database, Lyon, France, 2000.
[13] A. B. Singh, S. B. Singh and K. M. Singh, “Spam Classification Using Deep Learning Technique,” International Journal of Computer Sciences and Engineering, 31 May, Vol.6, No.5, pp.383-386, 2018.
[14] C. Neelam and D. Nitesh, “Spam Detection Approach Using Modified Pre-processing With NLP,” International Journal of Computer Sciences and Engineering, May, Vol.7, No.10, pp.158-161, 2019.
[15] H. A. Meaad, A. A. Mohammed and A. H. Mohd, “Advancing Email Spam Classification using Machine Learning and Deep Learning Technique,” Engineering, Technology & Applied Science Research, 14 May, Vol.14, No.4, pp.14994-15001, 2024.