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

In recent years, spammers are now trying to obfuscate their intents by introducing hybrid spam e-mail combining both image and text parts, which is more challenging to detect in comparison to e-mails containing text or image only. The motivation behind this research is to design an effective approach filtering out hybrid spam e-mails to avoid situations where traditional text-based or image-baesd only filters fail to detect hybrid spam e-mails. To the best of our knowledge, a few studies have been conducted with the goal of detecting hybrid spam e-mails. Ordinarily, Optical Character Recognition (OCR) technology is used to eliminate the image parts of spam by transforming images into text. However, the research questions are that although OCR scanning is a very successful technique in processing text-and-image hybrid spam, it is not an effective solution for dealing with huge quantities due to the CPU power required and the execution time it takes to scan e-mail files. And the OCR techniques are not always reliable in the transformation processes. To address such problems, we propose new late multi-modal fusion training frameworks for a text-and-image hybrid spam e-mail filtering system compared to the classical early fusion detection frameworks 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 sigmoid layer and Machine Learning based classifiers including Random Forest (RF), Decision Tree (DT), Naive Bayes (NB) and Support Vector Machine (SVM) to determine the e-mail ham or spam.Comment: Accepted by 2023 the 2nd International Conference on Mechatronics and Electrical Engineering (MEEE 2023

Hybrid approach for spam email detection

On this era, email is a convenient way to enable the user to communicate everywhere in the world which it has the internet. It is because of the economic and fast method of communication. The email message can send to the single user or distribute to the group. Majority of the users does not know the life exclusive of e-mail. For this issue, it becomes an email as the medium of communication of a malicious person. This project aimed at Spam Email. This project concentrated on a hybrid approach namely Neural Network (NN) and Particle Swarm Optimization (PSO) designed to detect the spam emails. The comparisons between the hybrid approach for NN_PSO with GA algorithm and NN classifiers to show the best performance for spam detection. The Spambase used contains 1813 as spams (39.40%) and 2788 as non-spam (60.6%) implemented on these algorithms. The comparisons performance criteria based on accuracy, false positive, false negative, precision, recall and f-measure. The feature selection used by applying GA algorithm to reducing the redundant and irrelevant features. The performance of F-Measure shows that the hybrid NN_PSO, GA_NN and NN are 94.10%, 92.60% and 91.39% respectively. The results recommended using the hybrid of NN_PSO with GA algorithm for the best performance for spam email detection

Hybrid Machine Learning Algorithms for Email and Malware Spam Filtering: A Review

In this paper, we presented a review of the state-of-the-art hybrid machine learning algorithms that were being used for email effective computing. For this reason, three research questions were formed, and the questions were answered by studying and analyzing related papers collected from some well-established scientific databases (Springer Link, IEEE Explore, Web of Science, and Scopus) based on some exclusion and inclusion criteria. The result presented the common Hybrid ML algorithms used to enhance email spam filtering. Also, the state-of-the-art datasets used for email and malware spam filtering were presented.&nbsp