An Improvised Email Spam Detection using FSSDL-ESDC Model
Keywords:
Email spams, Spam filtering, Machine learning, Feature selection, Classification, MetaheuristicsAbstract
Email is a commonly available communication technology used to share information among people via the Internet. But the drastic upsurge in email misuses/abuses has led to a rising quantity of spam emails in recent times. Spam email classification by the use of data mining and machine learning (ML) models has gained significant attention among researchers owing to the positive effect on saving Internet users. Different ML and feature selection (FS) techniques can be employed to design effective email spam detection and classification approaches. In this aspect, this paper devises a novel feature subset selection with deep learning-based email spam detection and classification (FSSDL-ESDC) technique. The FSSDL-ESDC technique encompasses two major processes namely tokenization and stops word removal. In addition, a feature selection approach based on fruitfly optimization (FFO) is used to find an optimum subset of characteristics. Furthermore, for the categorization of email spam, the bidirectional long short-term memory (BiLSTM) approach is applied. In order to boost the email spam detection performance of the BiLSTM model, grasshopper optimization algorithm (GOA) is applied to finely tune the hyper parameters of the BiLSTM model. The improved performance of the FSSDL-ESDC approach is shown by a rigorous simulation study. The experimental results demonstrated that the FSSDL-ESDC approach outperformed the other state-of-the-art procedures.
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