A language processing-free unified spam detection framework using byte histograms and deep learning | |
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Author | |
Abstract |
In this paper, we established a unified deep learning-based spam filtering method. The proposed method uses the message byte-histograms as a unified representation for all message types (text, images, or any other format). A deep convolutional neural network (CNN) is used to extract high-level features from this representation. A fully connected neural network is used to perform the classification using the extracted CNN features. We validate our method using several open-source text-based and image-based spam datasets.We obtained an accuracy higher than 94% on all datasets. |
Year of Publication |
2022
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Conference Name |
2022 Fourth International Conference on Transdisciplinary AI (TransAI)
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Google Scholar | BibTeX |