University of Hertfordshire

From the same journal

A Hybrid Spam Detection Method Based on Unstructured Datasets

Research output: Contribution to journalArticlepeer-review


  • Olga Angelopoulou
  • Shao Y
  • Trovati Marcello
  • Shi Q
  • Asimakopoulou E
  • Bessis Nik
View graph of relations
Original languageEnglish
Pages (from-to)233-243
Number of pages11
JournalSoft Computing
Early online date21 Dec 2015
Publication statusPublished - 1 Jan 2017


The identification of non-genuine or malicious messages poses a variety of challenges due to the continuous changes in the techniques utilised by cyber-criminals. In this article, we propose a hybrid detection method based on a combination of image and text spam recognition techniques. In particular, the former is based on sparse representation-based classification, which focuses on the global and local image features, and a dictionary learning technique to achieve a spam and a ham sub-dictionary. On the other hand, the textual analysis is based on semantic properties of documents to assess the level of maliciousness. More specifically, we are able to distinguish between meta-spam and real spam. Experimental results show the accuracy and potential of our approach.


This document is the accepted manuscript version of the following article: Shao, Y., Trovati, M., Shi, Q. et al. Soft Comput (2017) 21: 233. The final publication is available at Springer via © Springer-Verlag Berlin Heidelberg 2015.

ID: 11232867