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- W4360976225 abstract "Spam emails refer to unsolicited email messages, usually sent in bulk to a large list of recipients with the purpose of marketing, or luring individuals to download malware or click on phishing links. Although much research has been done on this topic, our study reveals that less attention is given to combatting the tactics used by spammers, like purposely forging spellings and the usage of smart sentence structuring. This work attempts to fill this void by using sub-wording and context-based methods. The sub-wording method is used to combat the usage of forged spellings while the context-based method is used to counter smart sentence structuring tactics used by spammers. Also, most of the prevalent methods have been evaluated only for one class weight. As the models’ performance can vary over class weights, we have used two class weights i.e., balanced and unbalanced for evaluation. The proposed model is designed by using a multi-head approach integrated with the Convolutional Neural Network and Bidirectional Gated Recurrent Unit network. The proposed model has been evaluated on the Lingspam and Spamtext datasets. Another issue neglected in prevalent methods is catastrophic forgetting, to consider it we have used a combination of the two datasets. The evaluation is done in terms of f1_score, accuracy, and receiver operator characteristic curve. The proposed model performs well, giving 99.75% accuracy for the Spamtext dataset and 99.79% accuracy for the Lingspam dataset. Obtained results are compared with state of art models available in the previous literature works." @default.
- W4360976225 created "2023-03-30" @default.
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- W4360976225 date "2023-01-01" @default.
- W4360976225 modified "2023-10-05" @default.
- W4360976225 title "Email Spam Detection Using Multi-head CNN-BiGRU Network" @default.
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- W4360976225 doi "https://doi.org/10.1007/978-3-031-28180-8_3" @default.
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