Welcome to the InfoSci Platform
A New Bio-Inspired Method for Spam Image-Based Emails Filtering

A New Bio-Inspired Method for Spam Image-Based Emails Filtering

Abdelkrim Latreche, Kadda Benyahia
Copyright: © 2021 |Volume: 11 |Issue: 2 |Pages: 22
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781799861171|DOI: 10.4018/IJOCI.2021040102
Cite Article Cite Article

MLA

Latreche, Abdelkrim, and Kadda Benyahia. "A New Bio-Inspired Method for Spam Image-Based Emails Filtering." IJOCI vol.11, no.2 2021: pp.29-50. http://doi.org/10.4018/IJOCI.2021040102

APA

Latreche, A. & Benyahia, K. (2021). A New Bio-Inspired Method for Spam Image-Based Emails Filtering. International Journal of Organizational and Collective Intelligence (IJOCI), 11(2), 29-50. http://doi.org/10.4018/IJOCI.2021040102

Chicago

Latreche, Abdelkrim, and Kadda Benyahia. "A New Bio-Inspired Method for Spam Image-Based Emails Filtering," International Journal of Organizational and Collective Intelligence (IJOCI) 11, no.2: 29-50. http://doi.org/10.4018/IJOCI.2021040102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Electronic mail has become one of the most popular and frequently used channels for personal and professional online communication. Despite its benefits, e-mail faces a major security problem, which is the daily reception of a large number of unsolicited electronic messages, known as “spam emails.” Today, most electronic mail systems have simple spam filtering mechanisms based on text spam filtering technologies. To circumvent these filters, spammers are introducing new techniques of embedding spam messages in the image attached to the mail. In this article, the authors propose a new method for spam image filtering. The proposed system can distinguish between legitimate images from spam images based on the texture characteristics of the image attached to an email. From each image, around 20 characteristics can be extracted from the gray level co-occurrence matrix (GLCM). Then, to classify the images as spam or ham, the authors use a new metaheuristic nature-inspired model for building classifiers based on the social worker bees and enhanced nearest-centroid classification method.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.