Detecting Spam Email with Machine Learning Optimized with Harris Hawks optimizer (HHO) Algorithm

Abstract

Email spam has been a big issue in recent years. As the percentage of internet users grows, so does the number of spam emails. Technologies are being used for illegitimate and immoral activities, such as phishing and robbery. As a consequence, it is essential to identify fraudulent spammers by employing machine learning techniques. This paper presents a novel spam classification technique that integrates the Harris Hawks optimizer (HHO) algorithm with the k-Nearest Neighbors algorithm (k-NN). The Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm motivated by Harris’ Hawks’ cooperative relations and surprises pounce pursue technique in nature. According to empirical results on the dataset, the suggested model can handle high-dimensional data (Spambase). The suggested model’s spam detection accuracy is compared to the numerous algorithms, including the Binary Dragonfly Algorithm (BDA), Equilibrium Optimizer (EO), Teaching-Learning-based Optimization, Seagull Optimization Algorithm (SO), and Marine Predators Algorithm (MPA). We found that the proposed technique trumps the other spam detection techniques investigated in this study in terms of classification accuracy. The experimental results showed that the proposed technique accuracy reaches %94.3.