A Hybrid Spam Detection Method Based on Unstructured Datasets
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.
Item Type | Article |
---|---|
Uncontrolled Keywords | Image spam, Text spam, Semantic networks, Classication, Subclass Discriminant Analysis, Feature Selection, Sparse Representation |
Date Deposited | 26 Jul 2024 13:15 |
Last Modified | 26 Jul 2024 13:16 |
Explore Further
Read more research from the creator(s):
Find work associated with the faculties and division(s):