| Title | A language processing-free unified spam detection framework using byte histograms and deep learning |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Belkhouche, Yassine |
| Conference Name | 2022 Fourth International Conference on Transdisciplinary AI (TransAI) |
| Keywords | artificial intelligence, byte histograms, convolutional neural network, convolutional neural networks, Deep Learning, feature extraction, Filtering, Histograms, Human Behavior, Metrics, Neural networks, pubcrawl, Scalability, spam detection |
| 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. |
| DOI | 10.1109/TransAI54797.2022.00021 |
| Citation Key | belkhouche_language_2022 |
A language processing-free unified spam detection framework using byte histograms and deep learning