General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Executive Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
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Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2023 Vol.15(2): 76-81
DOI: 10.7763/IJCTE.2023.V15.1334

A Late Multi-modal Fusion Model for Detecting Hybrid Spam E-mail

Zhibo Zhang*, Ernesto Damiani, Hussam Hamadi, Chan Yeun, and Fatma Taher

Manuscript received November 17, 2022; revised December 23, 2022; accepted February 2, 2023.

Abstract—In recent years, spammers are now trying to obfuscate spam filtering systems by introducing hybrid spam e-mail combining both image and text parts, which is more destructive and complicated compared to e-mails containing text or image only to cyber security. Traditionally, Optical Character Recognition (OCR) technology is used to eliminate the image parts of spam by transforming images into text. Although OCR scanning is a very successful technique for processing text-and-image hybrid spam, it is not an effective solution for dealing with huge quantities due to the Central Processing Unit (CPU) power required and the execution time it takes to scan e-mail files. To address this problem, this paper proposes a late multi-modal fusion model for a text-and-image hybrid spam e-mail filtering system compared to the classical early fusion detection model based on the OCR method. Convolutional Neural Network (CNN) and Continuous Bag of Words were implemented to extract features from image and text parts of hybrid spam respectively, whereas generated features were fed to the sigmoid layer and machine learning based classifiers to determine the e-mail ham or spam. The obtained two classification probability values were fed to a late decision model and the concluding classification decisions were analyzed with text-only classifiers based on the OCR technique in terms of prediction accuracy as well as computational efficiency. The experimental results show that the proposed late fusion model is highly superior to the benchmark in terms of execution time whereas other performance metrics are adequate. These findings reveal the superiorities of using CNN rather than OCR to detect hybrid spam e-mails.

Index Terms—Convolutional neural network, cyber security, hybrid spam e-mail, late fusion, spam filtering

Zhibo Zhang, Ernesto Damiani, Hussam Hamadi, and Chan Yeun are with Khalifa University, Abu Dhabi, UAE. Fatma Taher is with Zayed University, Dubai, UAE.
*Correspondence: (Z.Z.)


Cite:Zhibo Zhang, Ernesto Damiani, Hussam Hamadi, Chan Yeun, and Fatma Taher, "A Late Multi-modal Fusion Model for Detecting Hybrid Spam E-mail," International Journal of Computer Theory and Engineering vol. 15, no. 2, pp. 76-81, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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