An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network

Mohamad, M. and Abdullah, E.F.H.S. and Ghaleb, S.A.A. and Ghanem, W.A.H.M. (2021) An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network. In: 2nd International Conference on Advances in Cyber Security, 08-09 Dec 2020, Penang, Malaysia.

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Abstract

Email is an important communication that the Internet has made available. One of the significance is seen in the great ease in which immediate transmission of internet data is done during email transmission. This great ease emerges with a major issue which is the continuous increase in spam emails. Thus, the need for a spam email detector. The versatility and adaptability of the nature of spam influenced past innovations. However, previous techniques have been weakened. This study introduces an email detection model that is designed based on use of an improved version of the grasshopper optimization algorithm to train a Multilayer Perceptron in classifying emails as ham and spam. To validate the performance of EGOA, executed on the spam email dataset are utilized, then the performance was relatively compared with popular search algorithms. The implementation demonstrates that EGOA introduces the best results with high accuracy of up to 96.09%.

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