Abstract
The electronic mailing system has in recent years become a timely and convenient way for the exchange of multimedia messages across the cyberspace and computer networks in the global sphere. This proliferation has prompted most (if not all) inboxes receiving junk email messages on numerous occasions every day. Due to these surges in spam attacks, a number of approaches have been proposed to lessen the attacks across the globe significantly. The effect of previous detection techniques has been weakened due to the adaptive nature of unsolicited email spam. Hence, resolving spam detection (SD) problem is a challenging task. A regular class of the Artificial Neural Network (ANN) called Multi-Layer Perceptron (MLP) was proposed in this study for email SD. The main idea of this research is to train a neural network by leveraging a new nature-inspired metaheuristic algorithm referred to as a Grasshopper Optimization Algorithm (GOA) to categorize emails as ham and spam. Evaluation of its performance was performed on an often-used standard dataset. The results showed that the proposed MLP model trained by GOA achieves high accuracy of up to 94.25% performance compared to other optimization.
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Ghaleb, S.A.A., Mohamad, M., Abdullah, E.F.H.S., Ghanem, W.A.H.M. (2021). Spam Classification Based on Supervised Learning Using Grasshopper Optimization Algorithm and Artificial Neural Network. In: Anbar, M., Abdullah, N., Manickam, S. (eds) Advances in Cyber Security. ACeS 2020. Communications in Computer and Information Science, vol 1347. Springer, Singapore. https://doi.org/10.1007/978-981-33-6835-4_28
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