Detecting Spam Email with Machine Learning Optimized with Bio-Inspired Meta-Heuristic Algorithms

Gibson, Simran, Issac, Biju, Zhang, Li and Jacob, Seibu Mary (2020) Detecting Spam Email with Machine Learning Optimized with Bio-Inspired Meta-Heuristic Algorithms. IEEE Access. ISSN 2169-3536 (In Press)

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Official URL: https://doi.org/10.1109/ACCESS.2020.3030751

Abstract

Electronic mail has eased communication methods for many organisations as well as individuals. This method is exploited for fraudulent gain by spammers through sending unsolicited emails. This paper aims to present a method for detection of spam emails with machine learning algorithms that are optimized with bio-inspired methods. A literature review is carried to explore the efficient methods applied on different datasets to achieve good results. An extensive research was done to implement machine learning models using Naïve Bayes, Support Vector Machine, Random Forest, Decision Tree and Multi-Layer Perceptron on seven different email datasets, along with feature extraction and pre-processing. The bio-inspired algorithms like Particle Swarm Optimization and Genetic Algorithm were implemented to optimize the performance of classifiers. Multinomial Naïve Bayes with Genetic Algorithm performed the best overall. The comparison of our results with other machine learning and bio-inspired models to show the best suitable model is also discussed.

Item Type: Article
Uncontrolled Keywords: Machine Learning, Bio-inspired Algorithms, Cross-validation, Particle Swarm Optimiza-tion, Genetic Algorithm
Subjects: G400 Computer Science
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 12 Oct 2020 14:46
Last Modified: 14 Oct 2020 08:40
URI: http://nrl.northumbria.ac.uk/id/eprint/44488

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