Designing a novel framework of email spam detection using an improved heuristic algorithm and dual-scale feature fusion-based adaptive convolution neural network

Nowadays, e-mail is becoming more prevalent among individuals. In recent days, it has been declared to be the least expensive and quickest mode of communicating. In recent years, e-mail spam has become a big problem, so the number of e-mail spam has also increased, and they are used for unethical an...

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Vydané v:Information security journal. Ročník 34; číslo 4; s. 286 - 309
Hlavní autori: Kadam, Vikas S., Pingale, Subhash, Biradar, Sangappa R., Rohokale, Vandana M., Bamane, Kalyan D.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Taylor & Francis 04.07.2025
Predmet:
Dual scale feature fusion-based adaptive convolutional neural network
email spam detection
modified squid game optimizer
Vision Transformer
ISSN:1939-3555, 1939-3547
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Popis
Shrnutí:Nowadays, e-mail is becoming more prevalent among individuals. In recent days, it has been declared to be the least expensive and quickest mode of communicating. In recent years, e-mail spam has become a big problem, so the number of e-mail spam has also increased, and they are used for unethical and illegal conduct, scams, and theft. As a result, the main objective of the study is to detect e-mail spam using an adaptive deep-learning model with feature fusion. Initially, the input image and text are collected from benchmark datasets. Further, the raw text undergoes the pre-processing stage, and it is then followed by extracting the features using Bidirectional Encoder Representations from Transformers (BERT). Similarly, the Vision Transformer (ViT) is employed for extracting the features from raw images. Finally, these features are given in Dual Scale Feature Fusion-based Adaptive Convolutional Neural Network (DFF-ACNNet), and it is then fused for performing the classification in Convolutional Neural Network (CNN). Here, the hyper-parameters are tuned by using Modified Squid Game Optimizer (MSGO). Finally, the evaluation is done with various metrics to estimate the performance of the model and the accuracy of the developed system is 98.46, which is higher than the other conventional approaches.
ISSN:1939-3555
1939-3547
DOI:10.1080/19393555.2024.2432258